【大数据进阶第三阶段之Datax学习笔记】阿里云开源离线同步工具Datax概述
【大数据进阶第三阶段之Datax学习笔记】阿里云开源离线同步工具Datax快速入门
【大数据进阶第三阶段之Datax学习笔记】阿里云开源离线同步工具Datax类图
【大数据进阶第三阶段之Datax学习笔记】使用阿里云开源离线同步工具Datax实现数据同步
1、准备工作:
- JDK(1.8 以上,推荐 1.8)
- Python(23 版本都可以)
- Apache Maven 3.x(Compile DataX)(手动打包使用,使用
tar
包方式不需要安装)
主机名 | 操作系统 | IP 地址 | 软件包 |
MySQL-1 | CentOS 7.4 | 192.168.1.1 |
jdk-8u181-linux-x64.tar.gz datax.tar.gz
|
MySQL-2 | CentOS 7.4 | 192.168.1.2 |
2、安装 JDK:
下载地址:Java Archive Downloads - Java SE 8(需要创建 Oracle 账号)
[root@MySQL-1 ~]# ls anaconda-ks.cfg jdk-8u181-linux-x64.tar.gz [root@MySQL-1 ~]# tar zxf jdk-8u181-linux-x64.tar.gz [root@DataX ~]# ls anaconda-ks.cfg jdk1.8.0_181 jdk-8u181-linux-x64.tar.gz [root@MySQL-1 ~]# mv jdk1.8.0_181 /usr/local/java [root@MySQL-1 ~]# cat <<END >> /etc/profile export JAVA_HOME=/usr/local/java export PATH=$PATH:"$JAVA_HOME/bin" END [root@MySQL-1 ~]# source /etc/profile [root@MySQL-1 ~]# java -version
- 因为
CentOS 7
上自带Python 2.7
的软件包,所以不需要进行安装。
3、Linux 上安装 DataX 软件
[root@MySQL-1 ~]# wget http://datax-opensource.oss-cn-hangzhou.aliyuncs.com/datax.tar.gz [root@MySQL-1 ~]# tar zxf datax.tar.gz -C /usr/local/ [root@MySQL-1 ~]# rm -rf /usr/local/datax/plugin/*/._*
- 当未删除时,可能会输出:
[/usr/local/datax/plugin/reader/._drdsreader/plugin.json] 不存在. 请检查您的配置文件.
验证:
[root@MySQL-1 ~]# cd /usr/local/datax/bin [root@MySQL-1 ~]# python datax.py ../job/job.json
输出:
2021-12-13 19:26:28.828 [job-0] INFO JobContainer - PerfTrace not enable! 2021-12-13 19:26:28.829 [job-0] INFO StandAloneJobContainerCommunicator - Total 100000 records, 2600000 bytes | Speed 253.91KB/s, 10000 records/s | Error 0 records, 0 bytes | All Task WaitWriterTime 0.060s | All Task WaitReaderTime 0.068s | Percentage 100.00% 2021-12-13 19:26:28.829 [job-0] INFO JobContainer - 任务启动时刻 : 2021-12-13 19:26:18 任务结束时刻 : 2021-12-13 19:26:28 任务总计耗时 : 10s 任务平均流量 : 253.91KB/s 记录写入速度 : 10000rec/s 读出记录总数 : 100000 读写失败总数 : 0
4、DataX 基本使用
查看 streamreader \--> streamwriter
的模板:
[root@MySQL-1 ~]# python /usr/local/datax/bin/datax.py -r streamreader -w streamwriter
输出:
DataX (DATAX-OPENSOURCE-3.0), From Alibaba ! Copyright (C) 2010-2017, Alibaba Group. All Rights Reserved. Please refer to the streamreader document: https://github.com/alibaba/DataX/blob/master/streamreader/doc/streamreader.md Please refer to the streamwriter document: https://github.com/alibaba/DataX/blob/master/streamwriter/doc/streamwriter.md Please save the following configuration as a json file and use python {DATAX_HOME}/bin/datax.py {JSON_FILE_NAME}.json to run the job. { "job": { "content": [ { "reader": { "name": "streamreader", "parameter": { "column": [], "sliceRecordCount": "" } }, "writer": { "name": "streamwriter", "parameter": { "encoding": "", "print": true } } } ], "setting": { "speed": { "channel": "" } } } }
根据模板编写 json
文件
[root@MySQL-1 ~]# cat <<END > test.json { "job": { "content": [ { "reader": { "name": "streamreader", "parameter": { "column": [ # 同步的列名 (* 表示所有) { "type":"string", "value":"Hello." }, { "type":"string", "value":"河北彭于晏" }, ], "sliceRecordCount": "3" # 打印数量 } }, "writer": { "name": "streamwriter", "parameter": { "encoding": "utf-8", # 编码 "print": true } } } ], "setting": { "speed": { "channel": "2" # 并发 (即 sliceRecordCount * channel = 结果) } } } }
输出:(要是复制我上面的话,需要把 #
带的内容去掉)
5、安装 MySQL 数据库
分别在两台主机上安装:
[root@MySQL-1 ~]# yum -y install mariadb mariadb-server mariadb-libs mariadb-devel
[root@MySQL-1 ~]# systemctl start mariadb # 安装 MariaDB 数据库
[root@MySQL-1 ~]# mysql_secure_installation # 初始化
NOTE: RUNNING ALL PARTS OF THIS SCRIPT IS RECOMMENDED FOR ALL MariaDBSERVERS IN PRODUCTION USE! PLEASE READ EACH STEP CAREFULLY!Enter current password for root (enter for none): # 直接回车
OK, successfully used password, moving on...
Set root password? [Y/n] y # 配置 root 密码
New password:
Re-enter new password:
Password updated successfully!
Reloading privilege tables..... Success!
Remove anonymous users? [Y/n] y # 移除匿名用户... skipping.
Disallow root login remotely? [Y/n] n # 允许 root 远程登录... skipping.
Remove test database and access to it? [Y/n] y # 移除测试数据库... skipping.
Reload privilege tables now? [Y/n] y # 重新加载表... Success!
1)准备同步数据(要同步的两台主机都要有这个表)
MariaDB [(none)]> create database `course-study`;
Query OK, 1 row affected (0.00 sec)MariaDB [(none)]> create table `course-study`.t_member(ID int,Name varchar(20),Email varchar(30));
Query OK, 0 rows affected (0.00 sec)
因为是使用 DataX 程序进行同步的,所以需要在双方的数据库上开放权限:
grant all privileges on *.* to root@'%' identified by '123123';
flush privileges;
2)创建存储过程:
DELIMITER $$
CREATE PROCEDURE test()
BEGIN
declare A int default 1;
while (A < 3000000)do
insert into `course-study`.t_member values(A,concat("LiSa",A),concat("LiSa",A,"@163.com"));
set A = A + 1;
END while;
END $$
DELIMITER ;
3)调用存储过程(在数据源配置,验证同步使用):
call test();
6、通过 DataX 实 MySQL 数据同步
1)生成 MySQL 到 MySQL 同步的模板:
[root@MySQL-1 ~]# python /usr/local/datax/bin/datax.py -r mysqlreader -w mysqlwriter
{"job": {"content": [{"reader": {"name": "mysqlreader", # 读取端"parameter": {"column": [], # 需要同步的列 (* 表示所有的列)"connection": [{"jdbcUrl": [], # 连接信息"table": [] # 连接表}], "password": "", # 连接用户"username": "", # 连接密码"where": "" # 描述筛选条件}}, "writer": {"name": "mysqlwriter", # 写入端"parameter": {"column": [], # 需要同步的列"connection": [{"jdbcUrl": "", # 连接信息"table": [] # 连接表}], "password": "", # 连接密码"preSql": [], # 同步前. 要做的事"session": [], "username": "", # 连接用户 "writeMode": "" # 操作类型}}}], "setting": {"speed": {"channel": "" # 指定并发数}}}
}
2)编写 json
文件:
[root@MySQL-1 ~]# vim install.json
{"job": {"content": [{"reader": {"name": "mysqlreader", "parameter": {"username": "root","password": "123123","column": ["*"],"splitPk": "ID","connection": [{"jdbcUrl": ["jdbc:mysql://192.168.1.1:3306/course-study?useUnicode=true&characterEncoding=utf8"], "table": ["t_member"]}]}}, "writer": {"name": "mysqlwriter", "parameter": {"column": ["*"], "connection": [{"jdbcUrl": "jdbc:mysql://192.168.1.2:3306/course-study?useUnicode=true&characterEncoding=utf8","table": ["t_member"]}], "password": "123123","preSql": ["truncate t_member"], "session": ["set session sql_mode='ANSI'"], "username": "root", "writeMode": "insert"}}}], "setting": {"speed": {"channel": "5"}}}
}
3)验证
[root@MySQL-1 ~]# python /usr/local/datax/bin/datax.py install.json
输出:
2021-12-15 16:45:15.120 [job-0] INFO JobContainer - PerfTrace not enable!
2021-12-15 16:45:15.120 [job-0] INFO StandAloneJobContainerCommunicator - Total 2999999 records, 107666651 bytes | Speed 2.57MB/s, 74999 records/s | Error 0 records, 0 bytes | All Task WaitWriterTime 82.173s | All Task WaitReaderTime 75.722s | Percentage 100.00%
2021-12-15 16:45:15.124 [job-0] INFO JobContainer -
任务启动时刻 : 2021-12-15 16:44:32
任务结束时刻 : 2021-12-15 16:45:15
任务总计耗时 : 42s
任务平均流量 : 2.57MB/s
记录写入速度 : 74999rec/s
读出记录总数 : 2999999
读写失败总数 : 0
你们可以在目的数据库进行查看,是否同步完成。
- 上面的方式相当于是完全同步,但是当数据量较大时,同步的时候被中断,是件很痛苦的事情;
- 所以在有些情况下,增量同步还是蛮重要的。
7、使用 DataX 进行增量同步
使用 DataX 进行全量同步和增量同步的唯一区别就是:增量同步需要使用 where
进行条件筛选。(即,同步筛选后的 SQL)
1)编写 json
文件:
[root@MySQL-1 ~]# vim where.json
{"job": {"content": [{"reader": {"name": "mysqlreader", "parameter": {"username": "root","password": "123123","column": ["*"],"splitPk": "ID","where": "ID <= 1888","connection": [{"jdbcUrl": ["jdbc:mysql://192.168.1.1:3306/course-study?useUnicode=true&characterEncoding=utf8"], "table": ["t_member"]}]}}, "writer": {"name": "mysqlwriter", "parameter": {"column": ["*"], "connection": [{"jdbcUrl": "jdbc:mysql://192.168.1.2:3306/course-study?useUnicode=true&characterEncoding=utf8","table": ["t_member"]}], "password": "123123","preSql": ["truncate t_member"], "session": ["set session sql_mode='ANSI'"], "username": "root", "writeMode": "insert"}}}], "setting": {"speed": {"channel": "5"}}}
}
- 需要注意的部分就是:
where
(条件筛选) 和preSql
(同步前,要做的事) 参数。
2)验证:
[root@MySQL-1 ~]# python /usr/local/data/bin/data.py where.json
输出:
2021-12-16 17:34:38.534 [job-0] INFO JobContainer - PerfTrace not enable!
2021-12-16 17:34:38.534 [job-0] INFO StandAloneJobContainerCommunicator - Total 1888 records, 49543 bytes | Speed 1.61KB/s, 62 records/s | Error 0 records, 0 bytes | All Task WaitWriterTime 0.002s | All Task WaitReaderTime 100.570s | Percentage 100.00%
2021-12-16 17:34:38.537 [job-0] INFO JobContainer -
任务启动时刻 : 2021-12-16 17:34:06
任务结束时刻 : 2021-12-16 17:34:38
任务总计耗时 : 32s
任务平均流量 : 1.61KB/s
记录写入速度 : 62rec/s
读出记录总数 : 1888
读写失败总数 : 0
目标数据库上查看:
3)基于上面数据,再次进行增量同步:文章来源:https://www.toymoban.com/news/detail-821591.html
主要是 where 配置:"where": "ID > 1888 AND ID <= 2888" # 通过条件筛选来进行增量同步
同时需要将我上面的 preSql 删除(因为我上面做的操作时 truncate 表)
文章来源地址https://www.toymoban.com/news/detail-821591.html
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