其他
Flink 最佳实践之使用 Canal 同步 MySQL 数据至 TiDB
The following article is from Flink 中文社区 Author 王天宜@TIDB
背景介绍
环境介绍
部署 TiDB Cluster
部署 Zookeeper 环境
部署 Kafka
部署 Flink
部署 MySQL
部署 Canal
配置数据流向
一、背景介绍
为了能够快速的验证整套流程的功能性，所有的组件都以单机的形式部署。如果手上的物理资源不足，可以将本文中的所有组件搭建在一台 4G 1U 的虚拟机环境中。
如果需要在生产环境中部署，建议将每一个组件替换成高可用的集群部署方案。
其中，我们单独创建了一套 Zookeeper 单节点环境，Flink、Kafka、Canal 等组件共用这个 Zookeeper 环境。
针对于所有需要 JRE 的组件，如 Flink，Kafka，Canal，Zookeeper，考虑到升级 JRE 可能会影响到其他的应用，我们选择每个组件独立使用自己的 JRE 环境。
本文分为两个部分，其中，前七小节主要介绍基础环境的搭建，最后一个小节介绍了数据是如何在各个组件中流通的。
数据的流动经过以下组件：
MySQL 数据源生成 Binlog。
Canal 读取 Binlog，生成 Canal json，推送到 Kafka 指定的 Topic 中。
Flink 使用 flink-sql-connector-kafka API，消费 Kafka Topic 中的数据。
Flink 在通过 flink-connector-jdbc，将数据写入到 TiDB 中。
TiDB + Flink 的结构，支持开发与运行多种不同种类的应用程序。
目前主要的特性主要包括：
批流一体化。
精密的状态管理。
事件时间支持。
精确的一次状态一致性保障。
Flink 可以运行在包括 YARN、Mesos、Kubernetes 在内的多种资源管理框架上，还支持裸机集群上独立部署。TiDB 可以部署 AWS、Kubernetes、GCP GKE 上，同时也支持使用 TiUP 在裸机集群上独立部署。
TiDB + Flink 结构常见的几类应用如下：
事件驱动型应用：
反欺诈。
异常检测。
基于规则的报警。
业务流程监控。
数据分析应用：
网络质量监控。
产品更新及试验评估分析。
事实数据即席分析。
大规模图分析。
数据管道应用：
电商实时查询索引构建。
电商持续 ETL。
二、环境介绍
2.1 操作系统环境
[root@r20 topology]# cat /etc/redhat-releaseCentOS Stream release 82.2 软件环境
| Item | Version | Download link |
|---|---|---|
| TiDB | v4.0.9 | https://download.pingcap.org/tidb-community-server-v4.0.9-linux-amd64.tar.gz 1 |
| Kafka | v2.7.0 | https://mirrors.bfsu.edu.cn/apache/kafka/2.7.0/kafka_2.13-2.7.0.tgz |
| Flink | v1.12.1 | https://mirrors.tuna.tsinghua.edu.cn/apache/flink/flink-1.12.1/flink-1.12.1-bin-scala_2.11.tgz |
| Jre | v1.8.0_281 | https://javadl.oracle.com/webapps/download/AutoDL?BundleId=244058_89d678f2be164786b292527658ca1605 |
| Zookeeper | v3.6.2 | https://mirrors.tuna.tsinghua.edu.cn/apache/zookeeper/zookeeper-3.6.2/apache-zookeeper-3.6.2-bin.tar.gz |
| flink-sql-connector-kafka | v1.12.1 | https://repo1.maven.org/maven2/org/apache/flink/flink-sql-connector-kafka_2.12/1.12.0/flink-sql-connector-kafka_2.12-1.12.0.jar |
| flink-connector-jdbc | v1.12.0 | https://repo1.maven.org/maven2/org/apache/flink/flink-connector-jdbc_2.12/1.12.0/flink-connector-jdbc_2.12-1.12.0.jar |
| MySQL | v8.0.23 | https://dev.mysql.com/get/Downloads/MySQL-8.0/mysql-8.0.23-linux-glibc2.12-x86_64.tar.xz |
| Canal | v1.1.4 | https://github.com/alibaba/canal/releases/download/canal-1.1.4/canal.deployer-1.1.4.tar.gz |
2.3 机器分配
| Hostname | IP | Component |
r21 | 192.168.12.21 | TiDB Cluster |
r22 | 192.168.12.22 | Kafka |
r23 | 192.168.12.23 | Flink |
r24 | 192.168.12.24 | Zookeeper |
r25 | 192.168.12.25 | MySQL |
r26 | 192.168.12.26 | Canal |
三、部署 TiDB Cluster
与传统的单机数据库相比，TiDB 具有以下优势：
纯分布式架构，拥有良好的扩展性，支持弹性的扩缩容。
支持 SQL，对外暴露 MySQL 的网络协议，并兼容大多数 MySQL 的语法，在大多数场景下可以直接替换 MySQL。
默认支持高可用，在少数副本失效的情况下，数据库本身能够自动进行数据修复和故障转移，对业务透明。
支持 ACID 事务，对于一些有强一致需求的场景友好，例如：银行转账。
具有丰富的工具链生态，覆盖数据迁移、同步、备份等多种场景。
在内核设计上，TiDB 分布式数据库将整体架构拆分成了多个模块，各模块之间互相通信，组成完整的 TiDB 系统。对应的架构图如下：
在本文中，我们只做最简单的功能测试，所以部署了一套单节点但副本的 TiDB，涉及到了以下的三个模块：
TiDB Server：SQL 层，对外暴露 MySQL 协议的连接 endpoint，负责接受客户端的连接，执行 SQL 解析和优化，最终生成分布式执行计划。
PD (Placement Driver) Server：整个 TiDB 集群的元信息管理模块，负责存储每个 TiKV 节点实时的数据分布情况和集群的整体拓扑结构，提供 TiDB Dashboard 管控界面，并为分布式事务分配事务 ID。
TiKV Server：负责存储数据，从外部看 TiKV 是一个分布式的提供事务的 Key-Value 存储引擎。
3.1 TiUP 部署模板文件
# # Global variables are applied to all deployments and used as the default value of# # the deployments if a specific deployment value is missing.global: user: "tidb" ssh_port: 22 deploy_dir: "/opt/tidb-c1/" data_dir: "/opt/tidb-c1/data/"# # Monitored variables are applied to all the machines.#monitored:# node_exporter_port: 19100# blackbox_exporter_port: 39115# deploy_dir: "/opt/tidb-c3/monitored"# data_dir: "/opt/tidb-c3/data/monitored"# log_dir: "/opt/tidb-c3/log/monitored"# # Server configs are used to specify the runtime configuration of TiDB components.# # All configuration items can be found in TiDB docs:# # - TiDB: https://pingcap.com/docs/stable/reference/configuration/tidb-server/configuration-file/# # - TiKV: https://pingcap.com/docs/stable/reference/configuration/tikv-server/configuration-file/# # - PD: https://pingcap.com/docs/stable/reference/configuration/pd-server/configuration-file/# # All configuration items use points to represent the hierarchy, e.g:# # readpool.storage.use-unified-pool# ## # You can overwrite this configuration via the instance-level `config` field.server_configs: tidb: log.slow-threshold: 300 binlog.enable: false binlog.ignore-error: false tikv-client.copr-cache.enable: true tikv: server.grpc-concurrency: 4 raftstore.apply-pool-size: 2 raftstore.store-pool-size: 2 rocksdb.max-sub-compactions: 1 storage.block-cache.capacity: "16GB" readpool.unified.max-thread-count: 12 readpool.storage.use-unified-pool: false readpool.coprocessor.use-unified-pool: true raftdb.rate-bytes-per-sec: 0 pd: schedule.leader-schedule-limit: 4 schedule.region-schedule-limit: 2048 schedule.replica-schedule-limit: 64pd_servers: - host: 192.168.12.21 ssh_port: 22 name: "pd-2" client_port: 12379 peer_port: 12380 deploy_dir: "/opt/tidb-c1/pd-12379" data_dir: "/opt/tidb-c1/data/pd-12379" log_dir: "/opt/tidb-c1/log/pd-12379" numa_node: "0" # # The following configs are used to overwrite the `server_configs.pd` values. config: schedule.max-merge-region-size: 20 schedule.max-merge-region-keys: 200000tidb_servers: - host: 192.168.12.21 ssh_port: 22 port: 14000 status_port: 12080 deploy_dir: "/opt/tidb-c1/tidb-14000" log_dir: "/opt/tidb-c1/log/tidb-14000" numa_node: "0" # # The following configs are used to overwrite the `server_configs.tidb` values. config: log.slow-query-file: tidb-slow-overwrited.log tikv-client.copr-cache.enable: truetikv_servers: - host: 192.168.12.21 ssh_port: 22 port: 12160 status_port: 12180 deploy_dir: "/opt/tidb-c1/tikv-12160" data_dir: "/opt/tidb-c1/data/tikv-12160" log_dir: "/opt/tidb-c1/log/tikv-12160" numa_node: "0" # # The following configs are used to overwrite the `server_configs.tikv` values. config: server.grpc-concurrency: 4 #server.labels: { zone: "zone1", dc: "dc1", host: "host1" }#monitoring_servers:# - host: 192.168.12.21# ssh_port: 22# port: 19090# deploy_dir: "/opt/tidb-c1/prometheus-19090"# data_dir: "/opt/tidb-c1/data/prometheus-19090"# log_dir: "/opt/tidb-c1/log/prometheus-19090"#grafana_servers:# - host: 192.168.12.21# port: 13000# deploy_dir: "/opt/tidb-c1/grafana-13000"#alertmanager_servers:# - host: 192.168.12.21# ssh_port: 22# web_port: 19093# cluster_port: 19094# deploy_dir: "/opt/tidb-c1/alertmanager-19093"# data_dir: "/opt/tidb-c1/data/alertmanager-19093"# log_dir: "/opt/tidb-c1/log/alertmanager-19093"3.2 TiDB Cluster 环境
[root@r20 topology]# tiup cluster display tidb-c1-v409Starting component `cluster`: /root/.tiup/components/cluster/v1.3.2/tiup-cluster display tidb-c1-v409Cluster type: tidbCluster name: tidb-c1-v409Cluster version: v4.0.9SSH type: builtinDashboard URL: http://192.168.12.21:12379/dashboardID Role Host Ports OS/Arch Status Data Dir Deploy Dir-- ---- ---- ----- ------- ------ -------- ----------192.168.12.21:12379 pd 192.168.12.21 12379/12380 linux/x86_64 Up|L|UI /opt/tidb-c1/data/pd-12379 /opt/tidb-c1/pd-12379192.168.12.21:14000 tidb 192.168.12.21 14000/12080 linux/x86_64 Up - /opt/tidb-c1/tidb-14000192.168.12.21:12160 tikv 192.168.12.21 12160/12180 linux/x86_64 Up /opt/tidb-c1/data/tikv-12160 /opt/tidb-c1/tikv-12160Total nodes: 4创建用于测试的表mysql> show create table t1;+-------+-------------------------------------------------------------------------------------------------------------------------------+| Table | Create Table |+-------+-------------------------------------------------------------------------------------------------------------------------------+| t1 | CREATE TABLE `t1` ( `id` int(11) NOT NULL, PRIMARY KEY (`id`)) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin |+-------+-------------------------------------------------------------------------------------------------------------------------------+1 row in set (0.00 sec)四、部署 Zookeeper 环境
在本实验中单独配置 Zookeeper 环境，为 Kafka 和 Flink 环境提供服务。
作为实验演示方案，只部署单机环境。
4.1 解压 Zookeeper 包
[root@r24 soft]# tar vxzf apache-zookeeper-3.6.2-bin.tar.gz[root@r24 soft]# mv apache-zookeeper-3.6.2-bin /opt/zookeeper4.2 部署用于 Zookeeper 的 jre
[root@r24 soft]# tar vxzf jre1.8.0_281.tar.gz[root@r24 soft]# mv jre1.8.0_281 /opt/zookeeper/jre修改 /opt/zookeeper/bin/zkEnv.sh 文件，增加 JAVA_HOME 环境变量
## add bellowing env var in the head of zkEnv.shJAVA_HOME=/opt/zookeeper/jre4.3 创建 Zookeeper 的配置文件
[root@r24 conf]# cat zoo.cfg | grep -v "#"tickTime=2000initLimit=10syncLimit=5dataDir=/opt/zookeeper/dataclientPort=21814.4 启动 Zookeeper
[root@r24 bin]# /opt/zookeeper/bin/zkServer.sh start4.5 检查 Zookeeper 的状态
## check zk status[root@r24 bin]# ./zkServer.sh statusZooKeeper JMX enabled by defaultUsing config: /opt/zookeeper/bin/../conf/zoo.cfgClient port found: 2181. Client address: localhost. Client SSL: false.Mode: standalone## check OS port status[root@r24 bin]# netstat -ntlpActive Internet connections (only servers)Proto Recv-Q Send-Q Local Address Foreign Address State PID/Program nametcp 0 0 0.0.0.0:22 0.0.0.0:* LISTEN 942/sshdtcp6 0 0 :::2181 :::* LISTEN 15062/javatcp6 0 0 :::8080 :::* LISTEN 15062/javatcp6 0 0 :::22 :::* LISTEN 942/sshdtcp6 0 0 :::44505 :::* LISTEN 15062/java## use zkCli tool to check zk connection[root@r24 bin]# ./zkCli.sh -server 192.168.12.24:21814.6 关于 Zookeeper 的建议
我个人有一个关于 Zookeeper 的不成熟的小建议：
Zookeeper 集群版本一定要开启网络监控。特别是要关注 system metrics 里面的 network bandwidth。
五、部署 Kafka
Kafka 是一个分布式流处理平台，主要应用于两大类的应用中：
构造实时流数据管道，它可以在系统或应用之间可靠地获取数据。(相当于message queue)
构建实时流式应用程序，对这些流数据进行转换或者影响。(就是流处理，通过kafka stream topic和topic之间内部进行变化)
Kafka 有四个核心的 API：
The Producer API 允许一个应用程序发布一串流式的数据到一个或者多个Kafka topic。
The Consumer API 允许一个应用程序订阅一个或多个 topic ，并且对发布给他们的流式数据进行处理。
The Streams API 允许一个应用程序作为一个流处理器，消费一个或者多个topic产生的输入流，然后生产一个输出流到一个或多个topic中去，在输入输出流中进行有效的转换。
The Connector API 允许构建并运行可重用的生产者或者消费者，将Kafka topics连接到已存在的应用程序或者数据系统。比如，连接到一个关系型数据库，捕捉表（table）的所有变更内容。
在本实验中只做功能性验证，只搭建一个单机版的 Kafka 环境。
5.1 下载并解压 Kafka
[root@r22 soft]# tar vxzf kafka_2.13-2.7.0.tgz[root@r22 soft]# mv kafka_2.13-2.7.0 /opt/kafka5.2 部署用于 Kafka 的 jre
[root@r22 soft]# tar vxzf jre1.8.0_281.tar.gz[root@r22 soft]# mv jre1.8.0_281 /opt/kafka/jre修改 Kafka 的 jre 环境变量
[root@r22 bin]# vim /opt/kafka/bin/kafka-run-class.sh## add bellowing line in the head of kafka-run-class.shJAVA_HOME=/opt/kafka/jre5.3 修改 Kafka 配置文件
修改 Kafka 配置文件 /opt/kafka/config/server.properties
## change bellowing variable in /opt/kafka/config/server.propertiesbroker.id=0listeners=PLAINTEXT://192.168.12.22:9092log.dirs=/opt/kafka/logszookeeper.connect=i192.168.12.24:21815.4 启动 Kafka
[root@r22 bin]# /opt/kafka/bin/kafka-server-start.sh /opt/kafka/config/server.properties5.5 查看 Kafka 的版本信息
Kafka 并没有提供 --version 的 optional 来查看 Kafka 的版本信息。
[root@r22 ~]# ll /opt/kafka/libs/ | grep kafka-rw-r--r-- 1 root root 4929521 Dec 16 09:02 kafka_2.13-2.7.0.jar-rw-r--r-- 1 root root 821 Dec 16 09:03 kafka_2.13-2.7.0.jar.asc-rw-r--r-- 1 root root 41793 Dec 16 09:02 kafka_2.13-2.7.0-javadoc.jar-rw-r--r-- 1 root root 821 Dec 16 09:03 kafka_2.13-2.7.0-javadoc.jar.asc-rw-r--r-- 1 root root 892036 Dec 16 09:02 kafka_2.13-2.7.0-sources.jar-rw-r--r-- 1 root root 821 Dec 16 09:03 kafka_2.13-2.7.0-sources.jar.asc... ...其中 2.13 是 scale 的版本信息，2.7.0 是 Kafka 的版本信息。
六、部署 Flink
Apache Flink 是一个框架和分布式处理引擎，用于在无边界和有边界数据流上进行有状态的计算。Flink 能在所有常见集群环境中运行，并能以内存速度和任意规模进行计算。
支持高吞吐、低延迟、高性能的分布式处理框架 Apache Flink 是一个框架和分布式处理引擎，用于对无界和有界数据流进行有状态计算。Flink被设计在所有常见的集群环境中运行，以内存执行速度和任意规模来执行计算。
本实验只做功能性测试，仅部署单机 Flink 环境。
6.1 下载并分发 Flink
[root@r23 soft]# tar vxzf flink-1.12.1-bin-scala_2.11.tgz[root@r23 soft]# mv flink-1.12.1 /opt/flink6.2 部署 Flink 的 jre
[root@r23 soft]# tar vxzf jre1.8.0_281.tar.gz[root@r23 soft]# mv jre1.8.0_281 /opt/flink/jre6.3 添加 Flink 需要的 lib
Flink 消费 Kafka 数据，需要 flink-sql-connector-kafka 包。
Flink 链接 MySQL/TiDB，需要 flink-connector-jdbc 包。
[root@r23 soft]# mv flink-sql-connector-kafka_2.12-1.12.0.jar /opt/flink/lib/[root@r23 soft]# mv flink-connector-jdbc_2.12-1.12.0.jar /opt/flink/lib/6.4 修改 Flink 配置文件
## add or modify bellowing lines in /opt/flink/conf/flink-conf.yamljobmanager.rpc.address: 192.168.12.23env.java.home: /opt/flink/jre6.5 启动 Flink
[root@r23 ~]# /opt/flink/bin/start-cluster.shStarting cluster.Starting standalonesession daemon on host r23.Starting taskexecutor daemon on host r23.6.6 查看 Flink GUI
七、部署 MySQL
7.1 解压 MySQL package
[root@r25 soft]# tar vxf mysql-8.0.23-linux-glibc2.12-x86_64.tar.xz[root@r25 soft]# mv mysql-8.0.23-linux-glibc2.12-x86_64 /opt/mysql/7.2 创建 MySQL Service 文件
[root@r25 ~]# touch /opt/mysql/support-files/mysqld.service[root@r25 support-files]# cat mysqld.service[Unit]Description=MySQL 8.0 database serverAfter=syslog.targetAfter=network.target[Service]Type=simpleUser=mysqlGroup=mysql#ExecStartPre=/usr/libexec/mysql-check-socket#ExecStartPre=/usr/libexec/mysql-prepare-db-dir %n# Note: we set --basedir to prevent probes that might trigger SELinux alarms,# per bug #547485ExecStart=/opt/mysql/bin/mysqld_safe#ExecStartPost=/opt/mysql/bin/mysql-check-upgrade#ExecStopPost=/opt/mysql/bin/mysql-wait-stop# Give a reasonable amount of time for the server to start up/shut downTimeoutSec=300# Place temp files in a secure directory, not /tmpPrivateTmp=trueRestart=on-failureRestartPreventExitStatus=1# Sets open_files_limitLimitNOFILE = 10000# Set enviroment variable MYSQLD_PARENT_PID. This is required for SQL restart command.Environment=MYSQLD_PARENT_PID=1[Install]WantedBy=multi-user.target## copy mysqld.service to /usr/lib/systemd/system/[root@r25 support-files]# cp mysqld.service /usr/lib/systemd/system/7.3 创建 my.cnf 文件
[root@r34 opt]# cat /etc/my.cnf[mysqld]port=3306basedir=/opt/mysqldatadir=/opt/mysql/datasocket=/opt/mysql/data/mysql.socketmax_connections = 100default-storage-engine = InnoDBcharacter-set-server=utf8log-error = /opt/mysql/log/error.logslow_query_log = 1long-query-time = 30slow_query_log_file = /opt/mysql/log/show.logmin_examined_row_limit = 1000log-slow-slave-statementslog-queries-not-using-indexes#skip-grant-tables7.4 初始化并启动 MySQL
[root@r25 ~]# /opt/mysql/bin/mysqld --initialize --user=mysql --console[root@r25 ~]# chown -R mysql:mysql /opt/mysql[root@r25 ~]# systemctl start mysqld## check mysql temp passord from /opt/mysql/log/error.log2021-02-24T02:45:47.316406Z 6 [Note] [MY-010454] [Server] A temporary password is generated for root@localhost: I?nDjijxa3>-7.5 创建一个新的 MySQL 用户用以连接 Canal
## change mysql temp password firstlymysql> alter user 'root'@'localhost' identified by 'mysql';Query OK, 0 rows affected (0.00 sec)## create a management user 'root'@'%'mysql> create user 'root'@'%' identified by 'mysql';Query OK, 0 rows affected (0.01 sec)mysql> grant all privileges on *.* to 'root'@'%';Query OK, 0 rows affected (0.00 sec)## create a canal replication user 'canal'@'%'mysql> create user 'canal'@'%' identified by 'canal';Query OK, 0 rows affected (0.01 sec)mysql> grant select, replication slave, replication client on *.* to 'canal'@'%';Query OK, 0 rows affected (0.00 sec)mysql> flush privileges;Query OK, 0 rows affected (0.00 sec)7.6 在 MySQL 中创建用于测试的表
mysql> show create table test.t2;+-------+----------------------------------------------------------------------------------+| Table | Create Table |+-------+----------------------------------------------------------------------------------+| t2 | CREATE TABLE `t2` ( `id` int DEFAULT NULL) ENGINE=InnoDB DEFAULT CHARSET=utf8 |+-------+----------------------------------------------------------------------------------+1 row in set (0.00 sec)八、部署 Canal
Canal 主要用途是基于 MySQL 数据库增量日志解析，提供增量数据订阅和消费。
早期阿里巴巴因为杭州和美国双机房部署，存在跨机房同步的业务需求，实现方式主要是基于业务 trigger 获取增量变更。
从 2010 年开始，业务逐步尝试数据库日志解析获取增量变更进行同步，由此衍生出了大量的数据库增量订阅和消费业务。
基于日志增量订阅和消费的业务包括：
数据库镜像。
数据库实时备份。
索引构建和实时维护(拆分异构索引、倒排索引等)。
业务 cache 刷新。
带业务逻辑的增量数据处理。
当前的 canal 支持源端 MySQL 版本包括 5.1.x , 5.5.x , 5.6.x , 5.7.x , 8.0.x。
8.1 解压 Canal 包
[root@r26 soft]# mkdir /opt/canal && tar vxzf canal.deployer-1.1.4.tar.gz -C /opt/canal
8.2 部署 Canal 的 jre
[root@r26 soft]# tar vxzf jre1.8.0_281.tar.gz[root@r26 soft]# mv jre1.8.0_281 /opt/canal/jre## configue jre, add bellowing line in the head of /opt/canal/bin/startup.sh JAVA=/opt/canal/jre/bin/java8.3 修改 Canal 的配置文件
修改 /opt/canal/conf/canal.properties 配置文件
## modify bellowing configurationcanal.zkServers =192.168.12.24:2181canal.serverMode = kafkacanal.destinations = example ## 需要在 /opt/canal/conf 目录下创建一个 example 文件夹，用于存放 destination 的配置canal.mq.servers = 192.168.12.22:9092修改 /opt/canal/conf/example/instance.properties 配置文件## modify bellowing configurationcanal.instance.master.address=192.168.12.25:3306canal.instance.dbUsername=canalcanal.instance.dbPassword=canalcanal.instance.filter.regex=.*\\..* ## 过滤数据库的表canal.mq.topic=canal-kafka九、配置数据流向
9.1 MySQL Binlog -> Canal -> Kafka 通路
■ 9.1.1 查看 MySQL Binlog 信息
查看 MySQL Binlog 信息，确保 Binlog 是正常的。
mysql> show master status;+---------------+----------+--------------+------------------+-------------------+| File | Position | Binlog_Do_DB | Binlog_Ignore_DB | Executed_Gtid_Set |+---------------+----------+--------------+------------------+-------------------+| binlog.000001 | 2888 | | | |+---------------+----------+--------------+------------------+-------------------+1 row in set (0.00 sec)■ 9.1.2 在 Kafka 中创建一个 Topic
在 Kafka 中创建一个 Topic canal-kafka，这个Topic 的名字要与 Canal 配置文件 /opt/canal/conf/example/instance.properties 中的 canal.mq.topic=canal-kafka 对应：
[root@r22 kafka]# /opt/kafka/bin/kafka-topics.sh --create \> --zookeeper 192.168.12.24:2181 \> --config max.message.bytes=12800000 \> --config flush.messages=1 \> --replication-factor 1 \> --partitions 1 \> --topic canal-kafkaCreated topic canal-kafka.[2021-02-24 01:51:55,050] INFO [ReplicaFetcherManager on broker 0] Removed fetcher for partitions Set(canal-kafka-0) (kafka.server.ReplicaFetcherManager)[2021-02-24 01:51:55,052] INFO [Log partition=canal-kafka-0, dir=/opt/kafka/logs] Loading producer state till offset 0 with message format version 2 (kafka.log.Log)[2021-02-24 01:51:55,053] INFO Created log for partition canal-kafka-0 in /opt/kafka/logs/canal-kafka-0 with properties {compression.type -> producer, message.downconversion.enable -> true, min.insync.replicas -> 1, segment.jitter.ms -> 0, cleanup.policy -> [delete], flush.ms -> 9223372036854775807, segment.bytes -> 1073741824, retention.ms -> 604800000, flush.messages -> 1, message.format.version -> 2.7-IV2, file.delete.delay.ms -> 60000, max.compaction.lag.ms -> 9223372036854775807, max.message.bytes -> 12800000, min.compaction.lag.ms -> 0, message.timestamp.type -> CreateTime, preallocate -> false, min.cleanable.dirty.ratio -> 0.5, index.interval.bytes -> 4096, unclean.leader.election.enable -> false, retention.bytes -> -1, delete.retention.ms -> 86400000, segment.ms -> 604800000, message.timestamp.difference.max.ms -> 9223372036854775807, segment.index.bytes -> 10485760}. (kafka.log.LogManager)[2021-02-24 01:51:55,053] INFO [Partition canal-kafka-0 broker=0] No checkpointed highwatermark is found for partition canal-kafka-0 (kafka.cluster.Partition)[2021-02-24 01:51:55,053] INFO [Partition canal-kafka-0 broker=0] Log loaded for partition canal-kafka-0 with initial high watermark 0 (kafka.cluster.Partition)查看 Kafka 中所有的 Topic：
[root@r22 kafka]# /opt/kafka/bin/kafka-topics.sh --list --zookeeper 192.168.12.24:2181__consumer_offsetscanal-kafkaticdc-test查看 Kafka 中 Topic ticdc-test 的信息：
[root@r22 ~]# /opt/kafka/bin/kafka-topics.sh --describe --zookeeper 192.168.12.24:2181 --topic canal-kafkaTopic: ticdc-test PartitionCount: 1 ReplicationFactor: 1 Configs: max.message.bytes=12800000,flush.messages=1 Topic: ticdc-test Partition: 0 Leader: 0 Replicas: 0 Isr: 0■ 9.1.3 启动 Canal
在启动 Canal 之前，需要在 Canal 节点上查看一下端口的情况：
## check MySQL 3306 port## canal.instance.master.address=192.168.12.25:3306[root@r26 bin]# telnet 192.168.12.25 3306## check Kafka 9092 port## canal.mq.servers = 192.168.12.22:9092[root@r26 bin]# telnet 192.168.12.22 9092## check zookeeper 2181 port## canal.zkServers = 192.168.12.24:2181[root@r26 bin]# telnet 192.168.12.24 2181启动 Canal：
[root@r26 bin]# /opt/canal/bin/startup.shcd to /opt/canal/bin for workaround relative pathLOG CONFIGURATION : /opt/canal/bin/../conf/logback.xmlcanal conf : /opt/canal/bin/../conf/canal.propertiesCLASSPATH :/opt/canal/bin/../conf:/opt/canal/bin/../lib/zookeeper-3.4.5.jar:/opt/canal/bin/../lib/zkclient-0.10.jar:/opt/canal/bin/../lib/spring-tx-3.2.18.RELEASE.jar:/opt/canal/bin/../lib/spring-orm-3.2.18.RELEASE.jar:/opt/canal/bin/../lib/spring-jdbc-3.2.18.RELEASE.jar:/opt/canal/bin/../lib/spring-expression-3.2.18.RELEASE.jar:/opt/canal/bin/../lib/spring-core-3.2.18.RELEASE.jar:/opt/canal/bin/../lib/spring-context-3.2.18.RELEASE.jar:/opt/canal/bin/../lib/spring-beans-3.2.18.RELEASE.jar:/opt/canal/bin/../lib/spring-aop-3.2.18.RELEASE.jar:/opt/canal/bin/../lib/snappy-java-1.1.7.1.jar:/opt/canal/bin/../lib/snakeyaml-1.19.jar:/opt/canal/bin/../lib/slf4j-api-1.7.12.jar:/opt/canal/bin/../lib/simpleclient_pushgateway-0.4.0.jar:/opt/canal/bin/../lib/simpleclient_httpserver-0.4.0.jar:/opt/canal/bin/../lib/simpleclient_hotspot-0.4.0.jar:/opt/canal/bin/../lib/simpleclient_common-0.4.0.jar:/opt/canal/bin/../lib/simpleclient-0.4.0.jar:/opt/canal/bin/../lib/scala-reflect-2.11.12.jar:/opt/canal/bin/../lib/scala-logging_2.11-3.8.0.jar:/opt/canal/bin/../lib/scala-library-2.11.12.jar:/opt/canal/bin/../lib/rocketmq-srvutil-4.5.2.jar:/opt/canal/bin/../lib/rocketmq-remoting-4.5.2.jar:/opt/canal/bin/../lib/rocketmq-logging-4.5.2.jar:/opt/canal/bin/../lib/rocketmq-common-4.5.2.jar:/opt/canal/bin/../lib/rocketmq-client-4.5.2.jar:/opt/canal/bin/../lib/rocketmq-acl-4.5.2.jar:/opt/canal/bin/../lib/protobuf-java-3.6.1.jar:/opt/canal/bin/../lib/oro-2.0.8.jar:/opt/canal/bin/../lib/netty-tcnative-boringssl-static-1.1.33.Fork26.jar:/opt/canal/bin/../lib/netty-all-4.1.6.Final.jar:/opt/canal/bin/../lib/netty-3.2.2.Final.jar:/opt/canal/bin/../lib/mysql-connector-java-5.1.47.jar:/opt/canal/bin/../lib/metrics-core-2.2.0.jar:/opt/canal/bin/../lib/lz4-java-1.4.1.jar:/opt/canal/bin/../lib/logback-core-1.1.3.jar:/opt/canal/bin/../lib/logback-classic-1.1.3.jar:/opt/canal/bin/../lib/kafka-clients-1.1.1.jar:/opt/canal/bin/../lib/kafka_2.11-1.1.1.jar:/opt/canal/bin/../lib/jsr305-3.0.2.jar:/opt/canal/bin/../lib/jopt-simple-5.0.4.jar:/opt/canal/bin/../lib/jctools-core-2.1.2.jar:/opt/canal/bin/../lib/jcl-over-slf4j-1.7.12.jar:/opt/canal/bin/../lib/javax.annotation-api-1.3.2.jar:/opt/canal/bin/../lib/jackson-databind-2.9.6.jar:/opt/canal/bin/../lib/jackson-core-2.9.6.jar:/opt/canal/bin/../lib/jackson-annotations-2.9.0.jar:/opt/canal/bin/../lib/ibatis-sqlmap-2.3.4.726.jar:/opt/canal/bin/../lib/httpcore-4.4.3.jar:/opt/canal/bin/../lib/httpclient-4.5.1.jar:/opt/canal/bin/../lib/h2-1.4.196.jar:/opt/canal/bin/../lib/guava-18.0.jar:/opt/canal/bin/../lib/fastsql-2.0.0_preview_973.jar:/opt/canal/bin/../lib/fastjson-1.2.58.jar:/opt/canal/bin/../lib/druid-1.1.9.jar:/opt/canal/bin/../lib/disruptor-3.4.2.jar:/opt/canal/bin/../lib/commons-logging-1.1.3.jar:/opt/canal/bin/../lib/commons-lang3-3.4.jar:/opt/canal/bin/../lib/commons-lang-2.6.jar:/opt/canal/bin/../lib/commons-io-2.4.jar:/opt/canal/bin/../lib/commons-compress-1.9.jar:/opt/canal/bin/../lib/commons-codec-1.9.jar:/opt/canal/bin/../lib/commons-cli-1.2.jar:/opt/canal/bin/../lib/commons-beanutils-1.8.2.jar:/opt/canal/bin/../lib/canal.store-1.1.4.jar:/opt/canal/bin/../lib/canal.sink-1.1.4.jar:/opt/canal/bin/../lib/canal.server-1.1.4.jar:/opt/canal/bin/../lib/canal.protocol-1.1.4.jar:/opt/canal/bin/../lib/canal.prometheus-1.1.4.jar:/opt/canal/bin/../lib/canal.parse.driver-1.1.4.jar:/opt/canal/bin/../lib/canal.parse.dbsync-1.1.4.jar:/opt/canal/bin/../lib/canal.parse-1.1.4.jar:/opt/canal/bin/../lib/canal.meta-1.1.4.jar:/opt/canal/bin/../lib/canal.instance.spring-1.1.4.jar:/opt/canal/bin/../lib/canal.instance.manager-1.1.4.jar:/opt/canal/bin/../lib/canal.instance.core-1.1.4.jar:/opt/canal/bin/../lib/canal.filter-1.1.4.jar:/opt/canal/bin/../lib/canal.deployer-1.1.4.jar:/opt/canal/bin/../lib/canal.common-1.1.4.jar:/opt/canal/bin/../lib/aviator-2.2.1.jar:/opt/canal/bin/../lib/aopalliance-1.0.jar:cd to /opt/canal/bin for continue■ 9.1.4 查看 Canal 日志
查看 /opt/canal/logs/example/example.log
2021-02-24 01:41:40.293 [destination = example , address = /192.168.12.25:3306 , EventParser] WARN c.a.o.c.p.inbound.mysql.rds.RdsBinlogEventParserProxy - ---> begin to find start position, it will be long time for reset or first position2021-02-24 01:41:40.293 [destination = example , address = /192.168.12.25:3306 , EventParser] WARN c.a.o.c.p.inbound.mysql.rds.RdsBinlogEventParserProxy - prepare to find start position just show master status2021-02-24 01:41:40.542 [destination = example , address = /192.168.12.25:3306 , EventParser] WARN c.a.o.c.p.inbound.mysql.rds.RdsBinlogEventParserProxy - ---> find start position successfully, EntryPosition[included=false,journalName=binlog.000001,position=4,serverId=1,gtid=<null>,timestamp=1614134832000] cost : 244ms , the next step is binlog dump■ 9.1.5 查看 Kafka 中 consumer 信息
mysql> insert into t2 values(1);Query OK, 1 row affected (0.00 sec)/opt/kafka/bin/kafka-console-consumer.sh --bootstrap-server 192.168.12.22:9092 --topic canal-kafka --from-beginning{"data":null,"database":"test","es":1614151725000,"id":2,"isDdl":false,"mysqlType":null,"old":null,"pkNames":null,"sql":"create database test","sqlType":null,"table":"","ts":1614151725890,"type":"QUERY"}{"data":null,"database":"test","es":1614151746000,"id":3,"isDdl":true,"mysqlType":null,"old":null,"pkNames":null,"sql":"create table t2(id int)","sqlType":null,"table":"t2","ts":1614151746141,"type":"CREATE"}{"data":[{"id":"1"}],"database":"test","es":1614151941000,"id":4,"isDdl":false,"mysqlType":{"id":"int"},"old":null,"pkNames":null,"sql":"","sqlType":{"id":4},"table":"t2","ts":1614151941235,"type":"INSERT"}9.2 Kafka -> Flink 通路
## create a test table t2 in FlinkFlink SQL> create table t2(id int)> WITH (> 'connector' = 'kafka',> 'topic' = 'canal-kafka',> 'properties.bootstrap.servers' = '192.168.12.22:9092',> 'properties.group.id' = 'canal-kafka-consumer-group',> 'format' = 'canal-json',> 'scan.startup.mode' = 'latest-offset'> );Flink SQL> select * from t1;mysql> insert into test.t2 values(2);Query OK, 1 row affected (0.00 sec)Flink SQL> select * from t1; Refresh: 1 s Page: Last of 1 Updated: 02:49:27.366 id 29.3 Flink -> TiDB 通路
■ 9.3.1 在 下游的 TiDB 中创建用于测试的表
[root@r20 soft]# mysql -uroot -P14000 -hr21mysql> create table t3 (id int);Query OK, 0 rows affected (0.31 sec)
[root@r20 soft]# mysql -uroot -P14000 -hr21mysql> create table t3 (id int);Query OK, 0 rows affected (0.31 sec)■ 9.3.2 在 Flink 中创建测试表
Flink SQL> CREATE TABLE t3 (> id int> ) with (> 'connector' = 'jdbc',> 'url' = 'jdbc:mysql://192.168.12.21:14000/test',> 'table-name' = 't3',> 'username' = 'root',> 'password' = 'mysql'> );Flink SQL> insert into t3 values(3);[INFO] Submitting SQL update statement to the cluster...[INFO] Table update statement has been successfully submitted to the cluster:Job ID: a0827487030db177ee7e5c8575ef714e
Flink SQL> CREATE TABLE t3 (> id int> ) with (> 'connector' = 'jdbc',> 'url' = 'jdbc:mysql://192.168.12.21:14000/test',> 'table-name' = 't3',> 'username' = 'root',> 'password' = 'mysql'> );Flink SQL> insert into t3 values(3);[INFO] Submitting SQL update statement to the cluster...[INFO] Table update statement has been successfully submitted to the cluster:Job ID: a0827487030db177ee7e5c8575ef714e■ 9.3.3 在下游 TiDB 中查看插入的数据
mysql> select * from test.t3;+------+| id |+------+| 3 |+------+1 row in set (0.00 sec)
mysql> select * from test.t3;+------+| id |+------+| 3 |+------+1 row in set (0.00 sec)扩展阅读：【阿里巴巴大数据之路】资料，公众号“数据爱好者社区”后台回复“666”，转发即可下载。
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