博文原址：debezium关于cdc的使用(下)
简介
debezium在debezium关于cdc的使用(上)中有做介绍。具体可以跳到上文查看。本篇主要讲述使用kafka connector方式来同步数据。而kafka connector实际上也有提供其他的sink(Kafka Connect JDBC)来同步数据，但是没有delete事件。所以在这里选择了Debezium MySQL CDC Connector方式来同步。本文需要使用Avro方式序列化kafka数据。
流程
第一步准备
使用kafka消息中间介的话需要对应的服务支持，尤其需要chema-registry来管理schema，因电脑内存有限就没使用docker方式启动，如果条件ok内存够大的话阔以使用docker方式。所以使用的就是local本地方式。具体下载，安装，部署，配置环境变量我就不在重复描述了，阔以参考官方文档。
第二步启动kafka配套
进入目录后启动bin/confluent start
第三步创建kafka topic
可以通过kafka命令创建topic也可以通过Confluent Control Center 地址：http://localhost:9021来创建topic。我们还是按照上文的表来同步数据，所以创建topic：dbserver1.inventory.demo。
第四步创建kafka connect
可以通过kafka rest命令创建也可以使用Confluent Control Center创建。
方便点可以使用crul创建，以下为配置文件
{ "name": "inventory-connector", "config": { "connector.class": "io.debezium.connector.mysql.MySqlConnector", "tasks.max": "1", "database.hostname": "localhost", "database.port": "3306", "database.user": "debezium", "database.password": "dbz", "database.server.id": "184054", "database.server.name": "dbserver1", "database.whitelist": "inventory", "decimal.handling.mode": "double", "key.converter": "io.confluent.connect.avro.AvroConverter", "key.converter.schema.registry.url": "http://localhost:8081", "value.converter": "io.confluent.connect.avro.AvroConverter", "value.converter.schema.registry.url": "http://localhost:8081", "database.history.kafka.bootstrap.servers": "localhost:9092", "database.history.kafka.topic": "dbhistory.inventory" } } 创建好后可以使用命令查询到或者在管理中心查看。
命令：http://localhost:8083/connectors/inventory-connector
第五步启动同步程序
配置
spring: application: name: data-center datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://localhost:3306/inventory_back?useUnicode=true&characterEncoding=utf-8&useSSL=true&serverTimezone=UTC username: debe password: 123456 jpa: show-sql: true jackson: date-format: yyyy-MM-dd HH:mm:ss time-zone: GMT+8 # time-zone: UTC kafka: bootstrap-servers: localhost:9092 consumer: group-id: debezium-kafka-connector key-deserializer: "io.confluent.kafka.serializers.KafkaAvroDeserializer" value-deserializer: "io.confluent.kafka.serializers.KafkaAvroDeserializer" properties: schema.registry.url: http://localhost:8081
kafka消费者
跟上文的处理流程是一样的。只不过DDL和DML分成2个监听器。
package com.example.kakfa.avro; import com.example.kakfa.avro.sql.SqlProvider; import com.example.kakfa.avro.sql.SqlProviderFactory; import io.debezium.data.Envelope; import lombok.extern.slf4j.Slf4j; import org.apache.avro.generic.GenericData; import org.apache.commons.lang3.StringUtils; import org.apache.kafka.clients.consumer.ConsumerRecord; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.jdbc.core.JdbcTemplate; import org.springframework.jdbc.core.namedparam.NamedParameterJdbcTemplate; import org.springframework.kafka.annotation.KafkaListener; import org.springframework.stereotype.Component; import java.util.Objects; import java.util.Optional; @Slf4j @Component public class KafkaAvroConsumerRunner { @Autowired private JdbcTemplate jdbcTemplate; @Autowired private NamedParameterJdbcTemplate namedTemplate; @KafkaListener(id = "dbserver1-ddl-consumer", topics = "dbserver1") public void listenerUser(ConsumerRecord<GenericData.Record, GenericData.Record> record) throws Exception { GenericData.Record key = record.key(); GenericData.Record value = record.value(); log.info("Received record: {}", record); log.info("Received record: key {}", key); log.info("Received record: value {}", value); String databaseName = Optional.ofNullable(value.get("databaseName")).map(Object::toString).orElse(null); String ddl = Optional.ofNullable(value.get("ddl")).map(Object::toString).orElse(null); if (StringUtils.isBlank(ddl)) { return; } handleDDL(ddl, databaseName); } /** * 执行数据库ddl语句 * * @param ddl */ private void handleDDL(String ddl, String db) { log.info("ddl语句 : {}", ddl); try { if (StringUtils.isNotBlank(db)) { ddl = ddl.replace(db + ".", ""); ddl = ddl.replace("`" + db + "`.", ""); } jdbcTemplate.execute(ddl); } catch (Exception e) { log.error("数据库操作DDL语句失败，", e); } } @KafkaListener(id = "dbserver1-dml-consumer", topicPattern = "dbserver1.inventory.*") public void listenerAvro(ConsumerRecord<GenericData.Record, GenericData.Record> record) throws Exception { GenericData.Record key = record.key(); GenericData.Record value = record.value(); log.info("Received record: {}", record); log.info("Received record: key {}", key); log.info("Received record: value {}", value); if (Objects.isNull(value)) { return; } GenericData.Record source = (GenericData.Record) value.get("source"); String table = source.get("table").toString(); Envelope.Operation operation = Envelope.Operation.forCode(value.get("op").toString()); String db = source.get("db").toString(); handleDML(key, value, table, operation); } private void handleDML(GenericData.Record key, GenericData.Record value, String table, Envelope.Operation operation) { SqlProvider provider = SqlProviderFactory.getProvider(operation); if (Objects.isNull(provider)) { log.error("没有找到sql处理器提供者."); return; } String sql = provider.getSql(key, value, table); if (StringUtils.isBlank(sql)) { log.error("找不到sql."); return; } try { log.info("dml语句 : {}", sql); namedTemplate.update(sql, provider.getSqlParameterMap()); } catch (Exception e) { log.error("数据库DML操作失败，", e); } } } 数据流程
剩下的就是在inventory库中demo表中增删改数据，在对应的inventory_back库中demo表数据对应的改变。
欢迎关注微信公众号
