gravity 是摩拜单车出票的一个 异构/同构 数据复制通道软件，提供主流软件的支持，并支持k8s云原生。比较看好它的发展。
项目地址： https://github.com/moiot/gravity
官方文档：https://github.com/moiot/gravity/blob/master/docs/2.0/01-quick-start.md
gravity的编译和部署不是这里的重点，我们直接跳过。
gravity的部署：
cd /root/
git clone https://github.com/moiot/gravity.git
cd gravity && make
mkdir /usr/local/gravity/
cd /usr/local/gravity/
cp /root/gravity/bin/gravity /usr/local/gravity/
配置文件这里先忽略，
下面是我的架构图：
![image.png]( "1585213994949883.png")
业务场景：
一个老表，随着业务量增大，考虑到分表，按照 user_id 做hash取模拆分，然后业务层面去做数据CRUD操作。
数据表如下：
create database testdb;
use testdb;
CREATE TABLE `gravity_t1` (
`id` int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT '自增id',
`user_id` int(10) unsigned NOT NULL DEFAULT '0' COMMENT '用户id',
`s_status` tinyint(1) unsigned NOT NULL DEFAULT '0' COMMENT '状态',
PRIMARY KEY (`id`),
KEY `idx_uid` (`user_id`) USING BTREE
) COMMENT = '测试表' ENGINE=InnoDB AUTO_INCREMENT=1 DEFAULT CHARSET=utf8mb4;
准备拆分后的4个分表：
use testdb;
create table t1_shard1 LIKE gravity_t1 ;
create table t1_shard2 LIKE gravity_t1 ;
create table t1_shard3 LIKE gravity_t1 ;
create table t1_shard4 LIKE gravity_t1 ;
测试数据库连接方式：
数据库地址：192.168.2.4
超级账号： dts
密码： dts
假设业务用的普通账号叫rd ，密码无所谓。
造些测试用的数据：
for i in {1..10000} ; do
mysql -hdts -pdts -h 192.168.2.4 -e "insert into testdb.gravity_t1 (user_id,s_status) values (\"$RANDOM\",'0');"
done
结果大致这样：
[test] > select count(*) from gravity_t1 ;
+----------+
| count(*) |
+----------+
| 10000 |
+----------+
1 row in set (0.007 sec)
[testdb] > select (user_id%4) as hash_id,count(*) FROM gravity_t1 group by (user_id%4);
+---------+----------+
| hash_id | count(*) |
+---------+----------+
| 0| 2537 |
| 1 | 2419 |
| 2 | 2509 |
| 3| 2535 |
+---------+----------+
4 rows in set (0.009 sec)
shard1的配置文件，内容如下：
cat config_shard1.toml
# name 必填，这里保持每个配置文件的唯一性
name = "shard1"
# 内部用于保存位点、心跳等事项的库名，默认为 _gravity ， 实测发现这里改了名字也没用，保持默认即可
internal-db-name = "_gravity"
#
# Input 插件的定义，此处定义使用 mysql
#
[input]
type = "mysql"
mode = "replication"
[input.config.source]
host = "192.168.2.4"
username = "dts"
password = "dts"
port = 3306
#
# Output 插件的定义，此处使用 mysql
#
[output]
type = "mysql"
[output.config.target]
host = "192.168.2.4"
username = "dts"
password = "dts"
port = 3306
# 路由规则的定义
[[output.config.routes]]
match-schema = "testdb"
match-table = "gravity_t1"
target-schema = "testdb"
target-table = "t1_shard1"
# 这个target-table 代表的是需要写入到的分片名称，每个gravity实例的配置中都需要修改
开4个窗口演示：
cd /usr/local/gravity/
./bin/gravity -config config_shard1.toml -http-addr ":8083"
./bin/gravity -config config_shard2.toml -http-addr ":8184"
./bin/gravity -config config_shard3.toml -http-addr ":8185"
./bin/gravity -config config_shard4.toml -http-addr ":8186"
TIPS：
如果我们此时开了数据库的general_log的话， 能看到gravity到dest端是使用replace into方式插入全量数据的。然后再根据启动时候监听的binlog 实现增量数据的追平操作。
然后，看下 gravity 自动生成的库，存放都是和数据复制相关的信息：
[testdb] > show tables from _gravity ;
+----------------------+
| Tables_in__gravity |
+----------------------+
| gravity_heartbeat_v2 |
| gravity_positions |
+----------------------+
2 rows in set (0.000 sec)
[testdb] > select * from _gravity.gravity_heartbeat_v2;
+--------+--------+----------------------------+----------------------------+
| name | offset | update_time_at_gravity | update_time_at_source |
+--------+--------+----------------------------+----------------------------+
| shard1 | 57 | 2020-03-26 16:19:08.070483 | 2020-03-26 16:19:08.070589 |
| shard2 | 51 | 2020-03-26 16:19:07.469721 | 2020-03-26 16:19:07.469811 |
| shard3 | 50 | 2020-03-26 16:19:09.135751 | 2020-03-26 16:19:09.135843 |
| shard4 | 48 | 2020-03-26 16:19:08.448371 | 2020-03-26 16:19:08.448450 |
+--------+--------+----------------------------+----------------------------+
4 rows in set (0.001 sec)
[testdb] > select * from _gravity.gravity_positions\G
*************************** 1. row ***************************
name: shard1
stage: stream
position: {"current_position":{"binlog-name":"mysql-bin.000014","binlog-pos":28148767,"binlog-gtid":"fd2adbd9-e263-11e8-847a-141877487b3d:1-2600359"},"start_position":{"binlog-name":"mysql-bin.000014","binlog-pos":12866955,"binlog-gtid":"fd2adbd9-e263-11e8-847a-141877487b3d:1-2559919"}}
created_at: 2020-03-26 16:16:14
updated_at: 2020-03-26 16:19:26
*************************** 2. row ***************************
name: shard2
stage: stream
position: {"current_position":{"binlog-name":"mysql-bin.000014","binlog-pos":28155813,"binlog-gtid":"fd2adbd9-e263-11e8-847a-141877487b3d:1-2600366"},"start_position":{"binlog-name":"mysql-bin.000014","binlog-pos":16601348,"binlog-gtid":"fd2adbd9-e263-11e8-847a-141877487b3d:1-2569941"}}
created_at: 2020-03-26 16:16:31
updated_at: 2020-03-26 16:19:29
*************************** 3. row ***************************
name: shard3
stage: stream
position: {"current_position":{"binlog-name":"mysql-bin.000014","binlog-pos":28151964,"binlog-gtid":"fd2adbd9-e263-11e8-847a-141877487b3d:1-2600363"},"start_position":{"binlog-name":"mysql-bin.000014","binlog-pos":20333055,"binlog-gtid":"fd2adbd9-e263-11e8-847a-141877487b3d:1-2579960"}}
created_at: 2020-03-26 16:16:35
updated_at: 2020-03-26 16:19:29
*************************** 4. row ***************************
name: shard4
stage: stream
position: {"current_position":{"binlog-name":"mysql-bin.000014","binlog-pos":28152473,"binlog-gtid":"fd2adbd9-e263-11e8-847a-141877487b3d:1-2600364"},"start_position":{"binlog-name":"mysql-bin.000014","binlog-pos":24076960,"binlog-gtid":"fd2adbd9-e263-11e8-847a-141877487b3d:1-2589987"}}
created_at: 2020-03-26 16:16:40
updated_at: 2020-03-26 16:19:29
4 rows in set (0.000 sec)
TIPS：
到这一步，我们的4个分表的数据同步都配好了，我们可以再插入几条数据测试下。
-- insert into testdb.gravity_t1(user_id,s_status) values ('11111','0');
-- insert into testdb.gravity_t1(user_id,s_status) values ('11112','0');
-- 我这里演示就不插了
原始和拆分表的数据条数对比：
[testdb] > select (user_id%4) as hash_id,count(*) FROM gravity_t1 group by (user_id%4);
+---------+----------+
| hash_id | count(*) |
+---------+----------+
| 0 | 2537 |
| 1 | 2419 |
| 2 | 2509 |
| 3 | 2535 |
+---------+----------+
4 rows in set (0.009 sec
select count(*) FROM t1_shard1 where user_id%4=0;
select count(*) FROM t1_shard2 where user_id%4=1;
select count(*) FROM t1_shard3 where user_id%4=2;
select count(*) FROM t1_shard4 where user_id%4=3;
先做一次对分表中不需要的数据的删除操作，防止后期切换后删除数据量过大：
delete from t1_shard1 where user_id %4!=0;
delete from t1_shard2 where user_id %4!=1;
delete from t1_shard3 where user_id %4!=2;
delete from t1_shard4 where user_id %4!=3;
## 注意：生产环境大表的删除操作，建议使用pt-archiver进行
然后，再到原始表和分表中查询对比下数据是否一致：
select (user_id%4),count(*) as hash_id FROM gravity_t1 group by (user_id%4);
select count(*) FROM t1_shard1 where user_id%4=0;
select count(*) FROM t1_shard2 where user_id%4=1;
select count(*) FROM t1_shard3 where user_id%4=2;
select count(*) FROM t1_shard4 where user_id%4=3;
然后，等低峰期进行操作。
1、dba对涉及到的业务账号 对这个大表写权限回收掉
revoke insert,update,delete on testdb.gravity_t1 from rd@'%';
flush hosts;
flush tables;
2、通知业务方发版，切换数据库连接到4个新表
3、切换完成后，dba再执行一次删除各个分表脏数据的操作，
delete from t1_shard1 where user_id %4!=0;
delete from t1_shard2 where user_id %4!=1;
delete from t1_shard3 where user_id %4!=2;
delete from t1_shard4 where user_id %4!=3;
4、打开4个新表的写权限
GRANT select,insert,update,delete on testdb.t1_shard1 TO rd@'%';
GRANT select,insert,update,delete on testdb.t1_shard2 TO rd@'%';
GRANT select,insert,update,delete on testdb.t1_shard3 TO rd@'%';
GRANT select,insert,update,delete on testdb.t1_shard4 TO rd@'%';
5、然后，通知业务方测试。
6、业务方验证无问题后收工。至此，单表 拆分为分表的操作全部完成。
7、回退方案，待补充 （打开gravity的双向复制？？）
