简介：本文将介绍如何将 MySQL 中的数据，通过 Binlog + Canal 的模式导入到 Kafka 中，继而被 Flink 生产的案例。
一. 背景介绍
本文将介绍如何将 MySQL 中的数据，通过 Binlog + Canal 的模式导入到 Kafka 中，继而被 Flink 生产的案例。
为了可能疾速的验证整套流程的功能性，所有的组件都以单机的模式部署。如果手上的物理资源有余，能够将本文中的所有组件一台 4G 1U 的虚拟机环境中。
如果须要在生产环境中部署，倡议将每一个组件替换成高可用的集群部署计划。
其中，咱们独自创立了一套 Zookeeper 单节点环境，Flink、Kafka、Canal 等组件共用这个 Zookeeper 环境。
针对于所有须要 JRE 的组件，如 Flink，Kafka，Canal，Zookeeper，思考到降级 JRE 可能会影响到其余的利用，咱们抉择每个组件独立应用本人的 JRE 环境。
本文分为两个局部，其中，前七大节次要介绍根底环境的搭建，最初一个大节介绍了数据是如何在各个组件中流通的。
数据的流动通过以下组件：
- 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 应用 flink-sql-connector-kafka API，生产 Kafka Topic 中的数据。
- Flink 在通过 flink-connector-jdbc，将数据写入到 TiDB 中。
TiDB + Flink 的构造，反对开发与运行多种不同品种的应用程序。
目前次要的个性次要包含：
- 批流一体化。
- 精细的状态治理。
- 事件工夫反对。
- 准确的一次状态一致性保障。
Flink 能够运行在包含 YARN、Mesos、Kubernetes 在内的多种资源管理框架上，还反对裸机集群上独立部署。TiDB 能够部署 AWS、Kubernetes、GCP GKE 上，同时也反对应用 TiUP 在裸机集群上独立部署。
TiDB + Flink 构造常见的几类利用如下：
事件驱动型利用：
- 反欺诈。
- 异样检测。
- 基于规定的报警。
- 业务流程监控。
数据分析利用：
- 网络品质监控。
- 产品更新及试验评估剖析。
- 事实数据即席剖析。
- 大规模图剖析。
数据管道利用：
- 电商实时查问索引构建。
- 电商继续 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 机器调配
| 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 具备以下劣势：
- 纯分布式架构，领有良好的扩展性，反对弹性的扩缩容。
- 反对 SQL，对外裸露 MySQL 的网络协议，并兼容大多数 MySQL 的语法，在大多数场景下能够间接替换 MySQL。
- 默认反对高可用，在多数正本生效的状况下，数据库自身可能主动进行数据修复和故障转移，对业务通明。
- 反对 ACID 事务，对于一些有强统一需要的场景敌对，例如：银行转账。
- 具备丰盛的工具链生态，笼罩数据迁徙、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，各模块之间相互通信，组成残缺的 TiDB 零碎。对应的架构图如下：
在本文中，咱们只做最简略的功能测试，所以部署了一套单节点但正本的 TiDB，波及到了以下的三个模块：
- TiDB Server：SQL 层，对外裸露 MySQL 协定的连贯 endpoint，负责承受客户端的连贯，执行 SQL 解析和优化，最终生成分布式执行打算。
- 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状况和集群的整体拓扑构造，提供 TiDB Dashboard 管控界面，并为分布式事务调配事务 ID。
- TiKV Server：负责存储数据，从内部看 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
本文重点非部署 TiDB Cluster，作为疾速试验环境，只在一台机器上部署单正本的 TiDB Cluster 集群。不须要部署监控环境。
[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创立用于测试的表
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
在本试验中独自配置 Zookeeper 环境，为 Kafka 和 Flink 环境提供服务。
作为试验演示计划，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批改 /opt/zookeeper/bin/zkEnv.sh 文件，减少 JAVA\_HOME 环境变量
## add bellowing env var in the head of zkEnv.shJAVA_HOME=/opt/zookeeper/jre4.3 创立 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 查看 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 对于 Zookeeper 的倡议
我集体有一个对于 Zookeeper 的不成熟的小倡议：
Zookeeper 集群版本肯定要开启网络监控。特地是要关注 system metrics 外面的 network bandwidth。
五. 部署 Kafka
Kafka 是一个分布式流解决平台，次要利用于两大类的利用中：
- 结构实时流数据管道，它能够在零碎或利用之间牢靠地获取数据。 (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。 (就是流解决，通过kafka stream topic和topic之间外部进行变动)
Kafka 有四个外围的 API：
- The Producer API 容许一个应用程序公布一串流式的数据到一个或者多个Kafka topic。
- The Consumer API 容许一个应用程序订阅一个或多个 topic ，并且对公布给他们的流式数据进行解决。
- The Streams API 容许一个应用程序作为一个_流处理器_，生产一个或者多个topic产生的输出流，而后生产一个输入流到一个或多个topic中去，在输入输出流中进行无效的转换。
- The Connector API 容许构建并运行可重用的生产者或者消费者，将Kafka topics连贯到已存在的应用程序或者数据系统。比方，连贯到一个关系型数据库，捕获表（table）的所有变更内容。
在本试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批改 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 批改 Kafka 配置文件
批改 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解决引擎，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。
反对高吞吐、低提早、高性能的分布式解决框架 Apache Flink 是一个框架和分布式解决引擎，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。
本试验只做功能性测试，仅部署单机 Flink 环境。
6.1 下载并散发 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 增加 Flink 须要的 lib
Flink 生产 Kafka 数据，须要 flink-sql-connector-kafka 包。
Flink 链接 MySQL/TiDB，须要 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 批改 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 创立 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 创立 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 创立一个新的 MySQL 用户用以连贯 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 中创立用于测试的表
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生产。
晚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要，实现形式次要是基于业务 trigger 获取增量变更。
从 2010 年开始，业务逐渐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生产业务。
基于日志增量订阅和生产的业务包含：
- 数据库镜像。
- 数据库实时备份。
- 索引构建和实时保护(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。
以后的 canal 反对源端 MySQL 版本包含 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/canal8.2 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 批改 Canal 的配置文件
批改 /opt/canal/conf/canal.properties 配置文件
## modify bellowing configurationcanal.zkServers =192.168.12.24:2181canal.serverMode = kafkacanal.destinations = example ## 须要在 /opt/canal/conf 目录下创立一个 example 文件夹，用于寄存 destination 的配置canal.mq.servers = 192.168.12.22:9092批改 /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 是失常的。
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 中创立一个 Topic
在 Kafka 中创立一个 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: 09.1.3 启动 Canal
在启动 Canal 之前，须要在 Canal 节点上查看一下端口的状况：
## 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 continue9.1.4 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 dump9.1.5 查看 Kafka 中 consumer 信息
在 MySQL 中插入一条测试信息：
mysql> insert into t2 values(1);Query OK, 1 row affected (0.00 sec)查看 consumer 的信息，曾经有了方才插入的测试数据：
/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
在 Flink 中创立 t2 表，connector 类型为 kafka。
## 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;在 MySQL 中在插入一条测试数据：
mysql> insert into test.t2 values(2);Query OK, 1 row affected (0.00 sec)从 Flink 中能够实时同步数据：
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 上游的 TiDB 中创立用于测试的表
[root@r20 soft]# mysql -uroot -P14000 -hr21mysql> create table t3 (id int);Query OK, 0 rows affected (0.31 sec)9.3.2 在 Flink 中创立测试表
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: a0827487030db177ee7e5c8575ef714e9.3.3 在上游 TiDB 中查看插入的数据
mysql> select * from test.t3;+------+| id |+------+| 3 |+------+1 row in set (0.00 sec)版权申明：本文内容由阿里云实名注册用户自发奉献，版权归原作者所有，阿里云开发者社区不领有其著作权，亦不承当相应法律责任。具体规定请查看《阿里云开发者社区用户服务协定》和《阿里云开发者社区知识产权爱护指引》。如果您发现本社区中有涉嫌剽窃的内容，填写侵权投诉表单进行举报，一经查实，本社区将立即删除涉嫌侵权内容。