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【ClickHouse】Mysql数据同步 mysql+canal+kafka+ck

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  • Post category:mysql


https://blog.csdn.net/fzy629442466/article/details/122109956?share_token=7561c807-e86d-4ae9-ad8c-09ae99ca437e



1.MySQL配置打开bin-log日志



(1).查看状态
show variables like '%log_bin%';

在这里插入图片描述



(2).创建binlog存放目录并授权
mkdir /var/lib/mysql-bin
mkdir /var/lib/mysql-bin/log-bin
chown -R mysql:mysql /var/lib/mysql-bin
chmod -R 755 /var/lib/mysql-bin


(3).修改mysql配置文件

增加以下配置

vi /conf/my.cnf 

# server-id表示单个结点的id，这里由于只有一个结点，所以可以把id随机指定为一个数，这里将id设置成1。若集群中有多个结点，则id不能相同
server-id=1
# log-bin日志文件的名字为mysql-bin，以及其存储路径
log-bin=/var/lib/mysql-bin/log-bin/mysql-bin


(4).重启mysql，登录检查binlog状态
service mysql restart



2.canal-deployer安装配置



(1).解压安装包
tar -zxvf canal.deployer-1.1.5.tar.gz -C /usr/local/canal/canal-deployer


(2).配置监听MySQL

vi conf/example/instance.properties 

# position info
canal.instance.master.address=127.0.0.1:3306
canal.instance.master.journal.name=
canal.instance.master.position=
canal.instance.master.timestamp=
canal.instance.master.gtid=

canal.instance.dbUsername=root
canal.instance.dbPassword=root
canal.instance.connectionCharset = UTF-8

canal.instance.filter.regex=.*\\..*
  • filter 过滤 
canal.instance.filter.regex用于配置白名单，也就是我们希望订阅哪些库，哪些表，默认值为.*\\..*，也就是订阅所有库，所有表。
canal.instance.filter.black.regex用于配置黑名单，也就是我们不希望订阅哪些库，哪些表。没有默认值，也就是默认黑名单为空。

在这里插入图片描述



(3).启动
sh bin/startup.sh

//log下查看启动日志



3.Kafka模式

vi canal.properties 

改动 3 处
#################################################
#########       common argument     #############
#################################################
# 配置 服务IP
canal.ip = hadoop106
# 配置 模式
canal.serverMode = kafka
#################################################
#########      destinations         #############
#################################################
# 配置 目标实例
canal.destinations = example
##################################################
#########            Kafka           #############
##################################################
# 配置 Kafka 
kafka.bootstrap.servers = hadoop106:9092
kafka.acks = all
kafka.compression.type = none
kafka.batch.size = 16384
kafka.linger.ms = 1
kafka.max.request.size = 1048576
kafka.buffer.memory = 33554432
kafka.max.in.flight.requests.per.connection = 1
kafka.retries = 0
kafka.kerberos.enable = false
kafka.kerberos.krb5.file = "../conf/kerberos/krb5.conf"
kafka.kerberos.jaas.file = "../conf/kerberos/jaas.conf"

vi conf/example/instance.properties 

# 更改 kafka 消息主题
canal.mq.topic=employeeTopic

配置完成后重启canal-deployer



4.TCP模式-自定义canal-client



(1).修改canal配置

vi canal.properties 

# 配置 模式
canal.serverMode = tcp


(2).canal数据结构

在这里插入图片描述



(3).编写客户端代码


1).pom
    <dependencies>
        <dependency>
            <groupId>com.alibaba.otter</groupId>
            <artifactId>canal.client</artifactId>
            <version>1.1.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.4.1</version>
        </dependency>
    </dependencies>


2).客户端代码
public class CanalClient {
    public static void main(String[] args) throws Exception {
        //获取连接
        CanalConnector connector = CanalConnectors.newSingleConnector(new InetSocketAddress("1.1.1.1", 11111), "example", "", "");
        //连接
        connector.connect();
        //指定数据库
        connector.subscribe(".*..*");
        while (true) {
            //4.获取 Message
            Message message = connector.get(100);
            List<CanalEntry.Entry> entries = message.getEntries();
            if (entries.size() <= 0) {
                //没有数据等待
                System.out.println("没有数据，等待一下");
                try {
                    Thread.sleep(1000);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
            } else {
                for (CanalEntry.Entry entry : entries) {
                    //获取表名
                    String tableName = entry.getHeader().getTableName();
                    //类型
                    CanalEntry.EntryType entryType = entry.getEntryType();
                    if (entryType.equals(CanalEntry.EntryType.ROWDATA)) {
                        ByteString storeValue = entry.getStoreValue();
                        //反序列化
                        CanalEntry.RowChange rowChange = CanalEntry.RowChange.parseFrom(storeValue);
                        //获取事件的类型
                        CanalEntry.EventType eventType = rowChange.getEventType();
                        List<CanalEntry.RowData> rowDatasList = rowChange.getRowDatasList();
                        for (CanalEntry.RowData rowData : rowDatasList) {

                            JSONObject beforeData = new JSONObject();
                            List<CanalEntry.Column> beforeColumnsList = rowData.getBeforeColumnsList();
                            for (CanalEntry.Column column : beforeColumnsList) {
                                beforeData.put(column.getName(), column.getValue());
                            }

                            JSONObject afterData = new JSONObject();
                            List<CanalEntry.Column> afterColumnsList = rowData.getAfterColumnsList();
                            for (CanalEntry.Column column : afterColumnsList) {
                                afterData.put(column.getName(), column.getValue());
                            }
                            System.out.println("beforeData:" + beforeData + "afterData:" + afterData + "TableName:" + tableName + "eventType:" + eventType);
                        }
                    }
                }
            }
        }
    }
}



canal-adapter安装配置

略，使用较少



5.配置ClickHouse



(1).首先创建一张Kafka表引擎的表，用于从Kafka中读取数据
--  创建Kafka引擎表
 CREATE TABLE kafka_user_behavior_src (
    user_id UInt64 COMMENT '用户id',
    item_id UInt64 COMMENT '商品id',
    cat_id UInt16  COMMENT '品类id',
    action String  COMMENT '行为',
    province UInt8 COMMENT '省份id',
    ts UInt64      COMMENT '时间戳'
  ) ENGINE = Kafka()
    SETTINGS
    kafka_broker_list = 'cdh04:9092',
    kafka_topic_list = 'user_behavior',
    kafka_group_name = 'group1',
    kafka_format = 'JSONEachRow'
;


(2).创建一张普通表引擎的表，比如MergeTree，面向终端用户使用
 CREATE TABLE kafka_user_behavior (
    user_id UInt64 COMMENT '用户id',
    item_id UInt64 COMMENT '商品id',
    cat_id UInt16  COMMENT '品类id',
    action String  COMMENT '行为',
    province UInt8 COMMENT '省份id',
    ts UInt64      COMMENT '时间戳'
  ) ENGINE = MergeTree()
    ORDER BY user_id


(3).创建物化视图，用于将Kafka引擎表实时同步到终端用户所使用的表中
CREATE MATERIALIZED VIEW user_behavior_consumer TO kafka_user_behavior
    AS SELECT * FROM kafka_user_behavior_src ;


(4).查询数据
select * from kafka_user_behavior;



6.配置kafka

安装：见https://blog.csdn.net/qq_41466440/article/details/121327503



(1).创建topic
sh bin/kafka-topics.sh --bootstrap-server kafkahost:9092 --create --topic user_behavior --partitions 1


(2).生产消息
bin/kafka-console-producer.sh --broker-list kafkahost:9092 --topic user_behavior

{"user_id":63401,"item_id":6244,"cat_id":143,"action":"pv","province":3,"ts":1573445919}
{"user_id":9164,"item_id":2817,"cat_id":611,"action":"fav","province":28,"ts":1573420486}
{"user_id":63401,"item_id":6244,"cat_id":143,"action":"pv","province":3,"ts":1573445919}



7.经验

经验证，使用mysql+canal+kafka+ck模式并不会很适合mysql同步到ck的场景。

因：canal同步到kafka中的json数据格式其实已经不是想要同步到ck的数据格式了，它附加了一些其他属性信息

疑问：是否为配置的canal、kafka有问题，还请大佬指点。 

{"data":[{"id":"645304050783825920","department_code":"cyykjcxb","project_code":"bkml","adjust_time":"2022-01","adjust_range":"earlyMonth","amount":"5655.0","note":"112211","created_user":"lcbss@inspur.com","created_time":"2022-11-16 16:47:55","updated_user":"lcbss@inspur.com","updated_time":"2022-11-16 16:47:55"}],"database":"inspur_pss","es":1668669005000,"id":4,"isDdl":false,"mysqlType":{"id":"bigint(20)","department_code":"varchar(32)","project_code":"varchar(32)","adjust_time":"varchar(10)","adjust_range":"varchar(16)","amount":"decimal(20,8)","note":"varchar(1024)","created_user":"varchar(32)","created_time":"datetime","updated_user":"varchar(32)","updated_time":"datetime"},"old":[{"note":"11221"}],"pkNames":["id"],"sql":"","sqlType":{"id":-5,"department_code":12,"project_code":12,"adjust_time":12,"adjust_range":12,"amount":3,"note":12,"created_user":12,"created_time":93,"updated_user":12,"updated_time":93},"table":"profit_adjust","ts":1668669006066,"type":"UPDATE"}

基于当前情况，个人建议若要使用该架构，可以自己开发一个服务，监听kafka topic，将数据进行转换后写入ck。


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