DBLE分库分表实战
环境： DBLE 2.19.03.0
OS版本： CentOS Linux release 7.6.1810 (Core)
IP： 192.168.20.10/24
MySQL版本： MySQL-社区版-5.7.26
添加2个账号授权：
create user 'rw'@'%' identified by 'rw123456';
create user 'rd'@'%' identified by 'rd123456';
GRANT SELECT, INSERT, UPDATE, DELETE, CREATE,REFERENCES,CREATE TEMPORARY TABLES,INDEX ON *.* TO rw@'%' ;
GRANT SELECT ON *.* TO 'rd'@'%' ;连接方式：
读写：
mysql -urw -prw123456 --port 8066 -h 192.168.20.10 testdb
只读：
mysql -urd -prd123456 --port 8066 -h 192.168.20.10 testdb
ddl专用：
mysql -uop -p123456 --port 8066 -h 192.168.20.10 testdb
管理账号：
mysql -uman1 -p654321 --port 9066 -h 192.168.20.10
解压DBLE：
tar xf dble-2.19.03.tar.gz /usr/local/
cd /usr/local
ln -s dble-2.19.03 dble
cd conf/
vim schema.xml 修改后的如下：
<?xml version="1.0"?>
<!DOCTYPE dble:schema SYSTEM "schema.dtd">
<dble:schema xmlns:dble="http://dble.cloud/" version="2.19.03.0">
<schema name="testdb">
<!-- 全局表 -->
<table name="company" primaryKey="id" type="global" dataNode="dn1,dn2,dn3"/>
<!-- range分区2 -->
<table name="travelrecord" primaryKey="id" dataNode="dn1,dn2,dn3" rule="sharding-by-range_t"/>
<!-- hash mod 3 分区 -->
<table name="hotnews" primaryKey="id" dataNode="dn1,dn2,dn3" rule="id-sharding-by-mod3"/>
<!-- hashStringmod3 分区 -->
<table name="user_auth" primaryKey="open_id" dataNode="dn1,dn2,dn3" rule="user-auth-sharding-by-open_id" />
<!-- ER 分区 -->
<table name="order1" dataNode="dn1,dn2,dn3" rule="id-sharding-by-mod3">
<childTable name="order_detail" primaryKey="id" joinKey="order_id" parentKey="id" />
</table>
</schema>
<dataNode name="dn1" dataHost="192.168.20.10" database="db1"/>
<dataNode name="dn2" dataHost="192.168.20.10" database="db2"/>
<dataNode name="dn3" dataHost="192.168.20.10" database="db3"/>
<dataHost name="192.168.20.10" maxCon="500" minCon="10" balance="0" switchType="-1" slaveThreshold="100">
<heartbeat>select user()</heartbeat>
<writeHost host="hostM" url="192.168.20.10:3306" user="rw" password="rw123456">
<readHost host="hostS" url="192.168.20.10:3306" user="rd" password="rd123456"/>
</writeHost>
</dataHost>
</dble:schema>vim rule.xml 修改后的内容如下：
<tableRule name="sharding-by-range_t">
<rule>
<columns>id</columns>
<algorithm>rangeLong2</algorithm>
</rule>
</tableRule>
<tableRule name="id-sharding-by-mod3">
<rule>
<columns>id</columns>
<algorithm>hashmod3</algorithm>
</rule>
</tableRule>
<tableRule name="user-auth-sharding-by-open_id">
<rule>
<columns>open_id</columns>
<algorithm>hashStringmod3</algorithm>
</rule>
</tableRule>
<function name="rangeLong2" class="NumberRange">
<property name="mapFile">autopartition-long_t.txt</property>
<property name="defaultNode">0</property><!-- 不符合条件的插入到第一个分区去 -->
</function>
<function name="hashmod3" class="Hash">
<property name="partitionCount">3</property>
<property name="partitionLength">1</property>
</function>
<function name="hashStringmod3" class="StringHash">
<property name="partitionCount">3</property>
<property name="partitionLength">1</property>
<property name="hashSlice">0:20</property> <!-- 表示取前20位进行hash取模后再决定数据落在那个分片上 -->
</function>[root@centos7 /usr/local/dble/conf ]# vim autopartition-long_t.txt # 增加一个路由规则文件
# range start-end ,data node index
# K=1000,M=10000.
# 范围：前开后闭 (开区间，闭区间]
0-1M=0
1M-2M=1
2M-3M=2vim server.xml 内容如下：
修改user部分为如下：
<user name="man1">
<property name="password">654321</property>
<property name="manager">true</property>
<!-- manager user can't set schema-->
</user>
<user name="op">
<property name="password">123456</property>
<property name="schemas">testdb</property>
</user>
<!-- table's DML privileges INSERT/UPDATE/SELECT/DELETE -->
<!--
<privileges check="true">
<schema name="testdb" dml="0110" >
<table name="employee" dml="1111"></table>
</schema>
</privileges>
-->
<user name="rw">
<property name="password">rw123456</property>
<property name="schemas">testdb</property>
</user>
<user name="rd">
<property name="password">rd123456</property>
<property name="schemas">testdb</property>
<property name="readOnly">true</property>
</user>然后， reload 下 dble ， 进行测试
ddl专用：
mysql -uop -p123456 --port 8066 -h 192.168.20.10 testdb
去创建符合上面的要求的几个表,并写入数据测试：
## 测试range分区
(testdb) > create table travelrecord (
id bigint not null primary key,
user_id varchar(100),
traveldate DATE,
fee decimal(10,2),
days int
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
(testdb) > insert into travelrecord (id,user_id,traveldate,fee,days) values(10,'wang','2014-01-05',510,3);
(testdb) > insert into travelrecord (id,user_id,traveldate,fee,days) values(13000,'lee','2011-01-05',26.5,3);
(testdb) > insert into travelrecord (id,user_id,traveldate,fee,days) values(29800,'zhang','2018-01-05',23.3,3);
(testdb) > select * from travelrecord ;
+-------+---------+------------+--------+------+
| id | user_id | traveldate | fee | days |
+-------+---------+------------+--------+------+
| 10 | wang | 2014-01-05 | 510.00 | 3 |
| 13000 | lee | 2011-01-05 | 26.50 | 3 |
| 29800 | zhang | 2018-01-05 | 23.30 | 3 |
+-------+---------+------------+--------+------+## 测试全局表
(testdb) > create table company(id int not null primary key,name varchar(100));
(testdb) > insert into company(id,name) values(1,'hp');
(testdb) > insert into company(id,name) values(2,'ibm');
(testdb) > insert into company(id,name) values(3,'oracle');
(testdb) > select * from company ;
+----+--------+
| id | name |
+----+--------+
| 1 | hp |
| 2 | ibm |
| 3 | oracle |
+----+--------+
3 rows in set (0.01 sec)
多执行几次，你会看到三个分片上都插入了3条数据，因为company定义为全局表。
(testdb) > explain insert into company(id,name) values(1,'hp');
+-----------+----------+---------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------+----------+---------------------------------------------+
| dn1 | BASE SQL | insert into company(id,name) values(1,'hp') |
| dn2 | BASE SQL | insert into company(id,name) values(1,'hp') |
| dn3 | BASE SQL | insert into company(id,name) values(1,'hp') |
+-----------+----------+---------------------------------------------+
3 rows in set (0.00 sec)
使用 explain select * from company ; 命令也可以看到随机分发到3个节点的。## 测试hashmod分区
create table hotnews (id bigint unsigned not null primary key ,title varchar(400) ,created_time datetime) ENGINE=InnoDB DEFAULT CHARSET=utf8;
然后， 我们写个脚本，批量插入些数据，看看情况：
for i in {1..1000}; do
mysql -uop -p123456 --port 8066 -h 192.168.20.10 testdb -e "insert into hotnews(id,title,created_time) values($i,'one',now());"
done
然后，到后端的3个分片上看下数据量，大致如下，还是比较均匀的：
(db1) > select count(*) from db1.hotnews;
+----------+
| count(*) |
+----------+
| 333 |
+----------+
1 row in set (0.00 sec)
(db1) > select count(*) from db2.hotnews;
+----------+
| count(*) |
+----------+
| 334 |
+----------+
1 row in set (0.00 sec)
(db1) > select count(*) from db3.hotnews;
+----------+
| count(*) |
+----------+
| 333 |
+----------+
1 row in set (0.00 sec)## hashStringmod分区
CREATE TABLE `user_auth` (
`id` bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '主键id',
`open_id` varchar(100) NOT NULL DEFAULT '' COMMENT '第三方授权id',
`union_id` varchar(100) NOT NULL DEFAULT '' COMMENT '授权的关联id',
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='用户AUTH信息表' ;
#### 注意：实际生产环境的主键id需要由程序去保证唯一性（例如使用雪花算法）
(testdb) > insert into user_auth (id,open_id,union_id) values(1,'331116828422393856','oy0IAj9mdPUr7bLMl879Jp37eV3Y');
(testdb) > insert into user_auth (id,open_id,union_id) values(2,'341170994247204864','oy0IA3Yj9mdPUr7bLMl879Jp37eV');
(testdb) > insert into user_auth (id,open_id,union_id) values(3,'330414325695332352','oy0IAj9mdPU3Yr7bLMl879Jp37eV');
(testdb) > insert into user_auth (id,open_id,union_id) values(4,'328588424011591680','oy0IAj9mdPUr7bLMl8Jp37e79V');
(testdb) > insert into user_auth (id,open_id,union_id) values(5,'330414325695332352','oy0IA3Yj9mdPUr7p37ebLMl879JV3Y');
(testdb) > insert into user_auth (id,open_id,union_id) values(6,'341172222247211111','oy0IAj9bLMl879Jp37eV3YmdPUr7');
(testdb) > insert into user_auth (id,open_id,union_id) values(7,'341173334247755464','Jp37eoy0IAj9mdPUr73YbLMl879V');
(testdb) > select id,open_id,union_id from user_auth order by id asc ;
+----+--------------------+--------------------------------+
| id | open_id | union_id |
+----+--------------------+--------------------------------+
| 1 | 331116828422393856 | oy0IAj9mdPUr7bLMl879Jp37eV3Y |
| 2 | 341170994247204864 | oy0IA3Yj9mdPUr7bLMl879Jp37eV |
| 3 | 330414325695332352 | oy0IAj9mdPU3Yr7bLMl879Jp37eV |
| 4 | 328588424011591680 | oy0IAj9mdPUr7bLMl8Jp37e79V |
| 5 | 330414325695332352 | oy0IA3Yj9mdPUr7p37ebLMl879JV3Y |
| 6 | 341172222247211111 | oy0IAj9bLMl879Jp37eV3YmdPUr7 |
| 7 | 341173334247755464 | Jp37eoy0IAj9mdPUr73YbLMl879V |
+----+--------------------+--------------------------------+
7 rows in set (0.00 sec)
(testdb) > explain select id,open_id,union_id from user_auth where open_id = '341173334247755464' ;
+-----------+----------+--------------------------------------------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------+----------+--------------------------------------------------------------------------------+
| dn2 | BASE SQL | select id,open_id,union_id from user_auth where open_id = '341173334247755464' |
+-----------+----------+--------------------------------------------------------------------------------+
1 row in set (0.00 sec)
(testdb) > explain select id,open_id,union_id from user_auth where open_id = '331116828422393856' ;
+-----------+----------+--------------------------------------------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------+----------+--------------------------------------------------------------------------------+
| dn1 | BASE SQL | select id,open_id,union_id from user_auth where open_id = '331116828422393856' |
+-----------+----------+--------------------------------------------------------------------------------+
1 row in set (0.00 sec)
(testdb) > explain select id,open_id,union_id from user_auth where open_id = '328588424011591680' ;
+-----------+----------+--------------------------------------------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------+----------+--------------------------------------------------------------------------------+
| dn3 | BASE SQL | select id,open_id,union_id from user_auth where open_id = '328588424011591680' |
+-----------+----------+--------------------------------------------------------------------------------+
1 row in set (0.00 sec)上面就是几种常用的分区了， 另外还有种 date类型按时间分区的可能在日志表的场景下也常用些。
date类型分区的实验：
先去后端的db上创建物理的库：
create database userdb1 ;
create database userdb2 ;
create database userdb3 ;
create database userdb4 ;
create database userdb5 ;
create database userdb6 ;
create database userdb7 ;
create database userdb8 ;
create database userdb9 ;
create database userdb10 ;
create database userdb11 ;
create database userdb12 ;
create database userdb13 ;修改后的 schema.xml 类似如下：
<?xml version="1.0"?>
<!DOCTYPE dble:schema SYSTEM "schema.dtd">
<dble:schema xmlns:dble="http://dble.cloud/" version="2.19.03.0">
<schema name="testdb">
<!-- 按月分片 -->
<table name="user" dataNode="user_dn$1-13" rule="sharding-by-month-user"/>
</schema>
<dataNode name="user_dn1" dataHost="192.168.20.10" database="userdb1"/>
<dataNode name="user_dn2" dataHost="192.168.20.10" database="userdb2"/>
<dataNode name="user_dn3" dataHost="192.168.20.10" database="userdb3"/>
<dataNode name="user_dn4" dataHost="192.168.20.10" database="userdb4"/>
<dataNode name="user_dn5" dataHost="192.168.20.10" database="userdb5"/>
<dataNode name="user_dn6" dataHost="192.168.20.10" database="userdb6"/>
<dataNode name="user_dn7" dataHost="192.168.20.10" database="userdb7"/>
<dataNode name="user_dn8" dataHost="192.168.20.10" database="userdb8"/>
<dataNode name="user_dn9" dataHost="192.168.20.10" database="userdb9"/>
<dataNode name="user_dn10" dataHost="192.168.20.10" database="userdb10"/>
<dataNode name="user_dn11" dataHost="192.168.20.10" database="userdb11"/>
<dataNode name="user_dn12" dataHost="192.168.20.10" database="userdb12"/>
<dataNode name="user_dn13" dataHost="192.168.20.10" database="userdb13"/>
<dataHost name="192.168.20.10" maxCon="500" minCon="10" balance="0" switchType="-1" slaveThreshold="100">
<heartbeat>select user()</heartbeat>
<writeHost host="hostM" url="192.168.20.10:3306" user="rw" password="rw123456">
<readHost host="hostS" url="192.168.20.10:3306" user="rd" password="rd123456"/>
</writeHost>
</dataHost>
</dble:schema>然后，到 rule.xml中添加规则：
<tableRule name="sharding-by-month-user">
<rule>
<columns>addData</columns>
<algorithm>partbymonth-user</algorithm>
</rule>
</tableRule>
<!-- 加的基于月份的分片规则， 注意如果数量超了 会插入报错 -->
<function name="partbymonth-user" class="Date">
<property name="dateFormat">yyyy-MM-dd</property>
<property name="sBeginDate">2018-01-01</property>
<!-- <property name="sEndDate">2019-02-31</property> -->
<property name="sPartionDay">30</property> <!-- 默认是每10天一个分片。我这里改成每30天一个分片，另外注意并不按照固定的月来写入 -->
<property name="defaultNode">0</property><!-- 默认小于 2018-01-01 的数据插入到dn1去 -->
</function>(testdb) > create table if not exists user (addData date, dbname varchar(32),username varchar(32),province varchar(16),age int(3));
(testdb) > insert into user (addData,dbname,username,age) values ('2015-01-01',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2016-02-01',database(),'user1',12);
(testdb) > explain insert into user (addData,dbname,username,age) values ('2017-03-01',database(),'user1',12);
+-----------+----------+--------------------------------------------------------------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------+----------+--------------------------------------------------------------------------------------------------+
| user_dn1 | BASE SQL | INSERT INTO user (addData, dbname, username, age) VALUES ('2017-03-01', DATABASE(), 'user1', 12) |
+-----------+----------+--------------------------------------------------------------------------------------------------+
(testdb) > insert into user (addData,dbname,username,age) values ('2017-03-01',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-04-01',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-04-11',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-04-21',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-04-25',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-04-30',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-05-01',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-05-03',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-05-05',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-06-21',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2018-07-30',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2019-01-01',database(),'user1',12);
(testdb) > insert into user (addData,dbname,username,age) values ('2019-06-01',database(),'user1',12);
ERROR 1064 (HY000): can't find any valid data node :user -> ADDDATA -> 2019-06-01
因此，我们需要提前人工把分片加好 并做好可用分区的监控，不然会造成无法写入数据的事故出现。
(testdb) > select * from user order by addData asc ;
+------------+----------+----------+----------+------+
| addData | dbname | username | province | age |
+------------+----------+----------+----------+------+
| 2015-01-01 | userdb1 | user1 | NULL | 12 |
| 2016-02-01 | userdb1 | user1 | NULL | 12 |
| 2017-03-01 | userdb1 | user1 | NULL | 12 |
| 2018-04-01 | userdb4 | user1 | NULL | 12 |
| 2018-04-11 | userdb4 | user1 | NULL | 12 |
| 2018-04-21 | userdb4 | user1 | NULL | 12 |
| 2018-04-25 | userdb4 | user1 | NULL | 12 |
| 2018-04-30 | userdb4 | user1 | NULL | 12 |
| 2018-05-01 | userdb5 | user1 | NULL | 12 |
| 2018-05-03 | userdb5 | user1 | NULL | 12 |
| 2018-05-05 | userdb5 | user1 | NULL | 12 |
| 2018-06-21 | userdb6 | user1 | NULL | 12 |
| 2018-07-30 | userdb8 | user1 | NULL | 12 |
| 2019-01-01 | userdb13 | user1 | NULL | 12 |
+------------+----------+----------+----------+------+
14 rows in set (0.02 sec)
查询测试：
(testdb) > explain select * from user where addData between '2018-04-01' and '2018-04-30' ;
+-----------+----------+------------------------------------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------+----------+------------------------------------------------------------------------+
| user_dn4 | BASE SQL | select * from user where addData between '2018-04-01' and '2018-04-30' |
+-----------+----------+------------------------------------------------------------------------+
1 row in set (0.00 sec)
(testdb) > select * from user where addData between '2018-04-01' and '2018-04-30' ;
+------------+---------+----------+----------+------+
| addData | dbname | username | province | age |
+------------+---------+----------+----------+------+
| 2018-04-01 | userdb4 | user1 | NULL | 12 |
| 2018-04-11 | userdb4 | user1 | NULL | 12 |
| 2018-04-21 | userdb4 | user1 | NULL | 12 |
| 2018-04-25 | userdb4 | user1 | NULL | 12 |
| 2018-04-30 | userdb4 | user1 | NULL | 12 |
+------------+---------+----------+----------+------+
5 rows in set (0.01 sec)
(testdb) > explain select * from user where addData between '2018-04-01' and '2018-05-30' order by addData asc ;
+-----------------+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------------+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| user_dn4_0 | BASE SQL | select `user`.`addData`,`user`.`dbname`,`user`.`username`,`user`.`province`,`user`.`age` from `user` where addData BETWEEN '2018-04-01' AND '2018-05-30' ORDER BY `user`.`addData` ASC |
| user_dn5_0 | BASE SQL | select `user`.`addData`,`user`.`dbname`,`user`.`username`,`user`.`province`,`user`.`age` from `user` where addData BETWEEN '2018-04-01' AND '2018-05-30' ORDER BY `user`.`addData` ASC |
| merge_1 | MERGE | user_dn4_0; user_dn5_0 |
| shuffle_field_1 | SHUFFLE_FIELD | merge_1 |
+-----------------+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
4 rows in set (0.00 sec)
(testdb) > select * from user where addData between '2018-04-01' and '2018-05-30' order by addData asc ;
+------------+---------+----------+----------+------+
| addData | dbname | username | province | age |
+------------+---------+----------+----------+------+
| 2018-04-01 | userdb4 | user1 | NULL | 12 |
| 2018-04-11 | userdb4 | user1 | NULL | 12 |
| 2018-04-21 | userdb4 | user1 | NULL | 12 |
| 2018-04-25 | userdb4 | user1 | NULL | 12 |
| 2018-04-30 | userdb4 | user1 | NULL | 12 |
| 2018-05-01 | userdb5 | user1 | NULL | 12 |
| 2018-05-03 | userdb5 | user1 | NULL | 12 |
| 2018-05-05 | userdb5 | user1 | NULL | 12 |
+------------+---------+----------+----------+------+
8 rows in set (0.01 sec)date类型的可用分区的监控（脚本的原理同样适用于其他类型的分区）：
简单的做法就是定期执行一个explain的insert插入测试， 如果有ERROR关键字就告警出来
一个简单的脚本如下：
# 提前60天预警
DAYS=$(date -d 60days +%F)
echo $DAYS
if mysql -urw -prw123456 --port 8066 -h 192.168.20.10 testdb 2>/dev/null -e "explain insert into user (addData,dbname,username,age) values (\"$DAYS\",database(),'user1',12);" ; then
echo "当前可用分片数量处于安全状态"
else
echo "需要加新的分片了"
fidate类型加新的分片的方法：
1、修改schema.xml 加上新的分片的配置信息，修改后大致这样：
<?xml version="1.0"?>
<!DOCTYPE dble:schema SYSTEM "schema.dtd">
<dble:schema xmlns:dble="http://dble.cloud/" version="2.19.03.0">
<schema name="testdb">
<!-- 按月分片 -->
<table name="user" dataNode="user_dn$1-23" rule="sharding-by-month-user"/>
</schema>
<dataNode name="user_dn1" dataHost="192.168.20.10" database="userdb1"/>
<dataNode name="user_dn2" dataHost="192.168.20.10" database="userdb2"/>
<dataNode name="user_dn3" dataHost="192.168.20.10" database="userdb3"/>
<dataNode name="user_dn4" dataHost="192.168.20.10" database="userdb4"/>
<dataNode name="user_dn5" dataHost="192.168.20.10" database="userdb5"/>
<dataNode name="user_dn6" dataHost="192.168.20.10" database="userdb6"/>
<dataNode name="user_dn7" dataHost="192.168.20.10" database="userdb7"/>
<dataNode name="user_dn8" dataHost="192.168.20.10" database="userdb8"/>
<dataNode name="user_dn9" dataHost="192.168.20.10" database="userdb9"/>
<dataNode name="user_dn10" dataHost="192.168.20.10" database="userdb10"/>
<dataNode name="user_dn11" dataHost="192.168.20.10" database="userdb11"/>
<dataNode name="user_dn12" dataHost="192.168.20.10" database="userdb12"/>
<dataNode name="user_dn13" dataHost="192.168.20.10" database="userdb13"/>
<dataNode name="user_dn14" dataHost="192.168.20.10" database="userdb14"/>
<dataNode name="user_dn15" dataHost="192.168.20.10" database="userdb15"/>
<dataNode name="user_dn16" dataHost="192.168.20.10" database="userdb16"/>
<dataNode name="user_dn17" dataHost="192.168.20.10" database="userdb17"/>
<dataNode name="user_dn18" dataHost="192.168.20.10" database="userdb18"/>
<dataNode name="user_dn19" dataHost="192.168.20.10" database="userdb19"/>
<dataNode name="user_dn20" dataHost="192.168.20.10" database="userdb20"/>
<dataNode name="user_dn21" dataHost="192.168.20.10" database="userdb21"/>
<dataNode name="user_dn22" dataHost="192.168.20.10" database="userdb22"/>
<dataNode name="user_dn23" dataHost="192.168.20.10" database="userdb23"/>
<dataHost name="192.168.20.10" maxCon="500" minCon="10" balance="0" switchType="-1" slaveThreshold="100">
<heartbeat>select user()</heartbeat>
<writeHost host="hostM" url="192.168.20.10:3306" user="rw" password="rw123456">
<readHost host="hostS" url="192.168.20.10:3306" user="rd" password="rd123456"/>
</writeHost>
</dataHost>
</dble:schema>
2、重载配置文件
reload @@config_all ;
3、去后端创建对应的物理库
create database userdb14;
.....这里省略其它的建库语句.......
create database userdb23;
4、通过dble再次下发下建表命令
create table if not exists user (addData date, dbname varchar(32),username varchar(32),province varchar(16),age int(3));
5、插入数据测试
(testdb) > explain insert into user (addData,dbname,username,age) values ('2019-11-01',database(),'user1',12);
+-----------+----------+--------------------------------------------------------------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------+----------+--------------------------------------------------------------------------------------------------+
| user_dn23 | BASE SQL | INSERT INTO user (addData, dbname, username, age) VALUES ('2019-11-01', DATABASE(), 'user1', 12) |
+-----------+----------+--------------------------------------------------------------------------------------------------+
1 row in set (0.00 sec)
(testdb) > explain insert into user (addData,dbname,username,age) values ('2019-12-01',database(),'user1',12);
ERROR 1064 (HY000): can't find any valid data node :user -> ADDDATA -> 2019-12-01ER 表 （互联网场景下用多表JOIN的不多，因此ER分片规则不太常用到，但是需要大致的了解）：
下面的内容大篇幅参考： https://blog.csdn.net/zhanglei_16/article/details/50779929
1：ER分片关系简介
有一类业务，例如订单（ORDER）跟订单明细表（ORDER_DETAIL）,明细表会依赖订单单，就是该会存在表的主从关系，
这类似业务的切分可以抽象出合适的切分规则，比如根据用户ID切分,其它相关的表都依赖于用户ID，再或者根据订单ID进行切分，
总之部分业务总会可以抽象出父子关系的表。这类表适用于ER分片表，子表的记录与所关联的父表记录存放在同一个数据分片上，
避免数据Join跨库操作，以order与order_detail例子为例，schema.xml中定义合适的分片配置,order,order_detail 根据order_id
迕行数据切分，保证相同order_id的数据分到同一个分片上，在进行数据插入操作时，Mycat会获取order所在的分片，
然后将order_detail也插入到order所在的分片
2：父表按照主键ID分片，字表的分片字段与主表ID关联，配置为ＥＲ分片
2.1：在schema.xml添加如下配置配置文件修改
<!-- ER 分区 -->
<table name="order1" dataNode="dn1,dn2,dn3" rule="id-sharding-by-mod3">
<childTable name="order_detail" primaryKey="id" joinKey="order_id" parentKey="id" />
</table>在rule.xml里面设定分片规则：
<tableRule name="id-sharding-by-mod3">
<rule>
<columns>id</columns>
<algorithm>hashmod3</algorithm>
</rule>
</tableRule>
<!-- mod 3 -->
<function name="hashmod3" class="Hash">
<property name="partitionCount">3</property>
<property name="partitionLength">1</property>
</function>然后， reload 下 dble
2.2 先建表， order 和 order_detail 表，有主外键关系
mysql> explain CREATE TABLE order1 (id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,sn VARCHAR(64),create_time DATETIME) ENGINE=InnoDB DEFAULT CHARSET=utf8;
+-----------+-----------------------------------------------------------------------------------------------------+
| DATA_NODE | SQL |
+-----------+-----------------------------------------------------------------------------------------------------+
| dn1 | CREATE TABLE order1(id int unsigned NOT NULL AUTO_INCREMENT PRIMARY KEY,sn VARCHAR(64),create_time DATETIME) |
| dn2 | CREATE TABLE order1(id int unsigned NOT NULL AUTO_INCREMENT PRIMARY KEY,sn VARCHAR(64),create_time DATETIME) |
| dn3 | CREATE TABLE order1(id int unsigned NOT NULL AUTO_INCREMENT PRIMARY KEY,sn VARCHAR(64),create_time DATETIME) |
+-----------+-----------------------------------------------------------------------------------------------------+
3 rows in set (0.02 sec)
mysql> CREATE TABLE order1(id INT NOT NULL AUTO_INCREMENT PRIMARY KEY,sn VARCHAR(64),create_time DATETIME) ENGINE=InnoDB DEFAULT CHARSET=utf8;
Query OK, 0 rows affected (0.35 sec)
mysql> CREATE TABLE order_detail(id INT AUTO_INCREMENT PRIMARY KEY, order_id INT,ord_status CHAR(1),address VARCHAR(128),create_time DATETIME,CONSTRAINT FK_ORDid FOREIGN KEY (order_id) REFERENCES order1 (id)) ENGINE=InnoDB DEFAULT CHARSET=utf8;
Query OK, 0 rows affected (0.44 sec)3.3 录入数据：
mysql> explain INSERT INTO order1(id,sn,create_time) VALUES(1,'BJ0001',NOW());
+-----------+----------------------------------------------------------------+
| DATA_NODE | SQL |
+-----------+----------------------------------------------------------------+
| dn2 | INSERT INTO order1(id,sn,create_time) VALUES(1,'BJ0001',NOW()) |
+-----------+----------------------------------------------------------------+
1 row in set (0.03 sec)录入数据，一组组录入，涉及到外键关系：
第一组北京的订单
mysql> INSERT INTO order1(id,sn,create_time) VALUES(1,'BJ0001',NOW());
Query OK, 1 row affected (0.05 sec)
mysql> INSERT INTO ORDER_DETAIL(id,order_id,ord_status,address,create_time) VALUES (1,1,'1','test data of order1(id=1,BJ001) ',NOW());第二组上海的订单：
mysql> explain INSERT INTO order1(id,sn,create_time) VALUES(3,'SHH001',NOW());
+-----------+----------------------------------------------------------------+
| DATA_NODE | SQL |
+-----------+----------------------------------------------------------------+
| dn1 | INSERT INTO order1(id,sn,create_time) VALUES(3,'SHH001',NOW()) |
+-----------+----------------------------------------------------------------+
1 row in set (0.02 sec)
mysql> INSERT INTO order1(id,sn,create_time) VALUES(3,'SHH001',NOW());
Query OK, 1 row affected (0.04 sec)
mysql> INSERT INTO ORDER_DETAIL(id,order_id,ord_status,address,create_time) VALUES (3,3,'1','test data of order1(id=3,SHH001)',NOW());
Query OK, 1 row affected (0.06 sec)第三组广州的订单：
mysql> explain INSERT INTO order1(id,sn,create_time) VALUES(4,'GZH004',NOW());
+-----------+----------------------------------------------------------------+
| DATA_NODE | SQL |
+-----------+----------------------------------------------------------------+
| dn2 | INSERT INTO order1(id,sn,create_time) VALUES(4,'GZH004',NOW()) |
+-----------+----------------------------------------------------------------+
1 row in set (0.00 sec)
mysql> INSERT INTO order1(id,sn,create_time) VALUES(4,'GZH004',NOW());
Query OK, 1 row affected (0.06 sec)
mysql> INSERT INTO ORDER_DETAIL(id,order_id,ord_status,address,create_time) VALUES (4,4,'1','test data of order1(id=4,GZH004) ',NOW());
Query OK, 1 row affected (0.05 sec)第四组 武汉的订单，这里故意将order_id设置成4，看看效果，是否随id为4的广州的那组分片：
mysql> explain INSERT INTO order1(id,sn,create_time) VALUES(5,'WUHAN005',NOW());
+-----------+------------------------------------------------------------------+
| DATA_NODE | SQL |
+-----------+------------------------------------------------------------------+
| dn3 | INSERT INTO order1(id,sn,create_time) VALUES(5,'WUHAN005',NOW()) |
+-----------+------------------------------------------------------------------+
1 row in set (0.01 sec)
mysql> explain INSERT INTO order1(id,sn,create_time) VALUES(6,'WUHAN006',NOW());
Query OK, 1 row affected (0.03 sec)
mysql> INSERT INTO ORDER_DETAIL(id,order_id,ord_status,address,create_time) VALUES (6,4,'1','test data of order1(id=6,WUHAN006) ',NOW());
Query OK, 1 row affected (0.05 sec)通过DBLE，查看下数据写入的情况：
(testdb) > select * from order1;
+----+--------+---------------------+
| id | sn | create_time |
+----+--------+---------------------+
| 1 | BJ0001 | 2019-08-31 23:05:36 |
| 4 | GZH004 | 2019-08-31 23:06:57 |
| 3 | SHH001 | 2019-08-31 23:06:43 |
+----+--------+---------------------+
3 rows in set (0.01 sec)
(testdb) > select * from order_detail ;
+----+----------+------------+--------------------------------------+---------------------+
| id | order_id | ord_status | address | create_time |
+----+----------+------------+--------------------------------------+---------------------+
| 1 | 1 | 1 | test data of ORDER1(ID=1,BJ001) | 2019-08-31 23:06:17 |
| 4 | 4 | 1 | test data of ORDER1(ID=4,GZH004) | 2019-08-31 23:07:01 |
| 6 | 4 | 1 | test data of ORDER1(ID=6,WUHAN006) | 2019-08-31 23:07:23 |
| 3 | 3 | 1 | test data of ORDER1(ID=3,SHH001) | 2019-08-31 23:06:47 |
+----+----------+------------+--------------------------------------+---------------------+
4 rows in set (0.01 sec)直连后端的db1，看下数据情况 (db2 和 db3 上面的数据查看，使用同样的方法）；
((none)) > select * from db1.order1;
+----+--------+---------------------+
| id | sn | create_time |
+----+--------+---------------------+
| 3 | SHH001 | 2019-08-31 23:06:43 |
+----+--------+---------------------+
1 row in set (0.00 sec)
((none)) > select * from db1.order_detail;
+----+----------+------------+----------------------------------+---------------------+
| id | order_id | ord_status | address | create_time |
+----+----------+------------+----------------------------------+---------------------+
| 3 | 3 | 1 | test data of ORDER1(ID=3,SHH001) | 2019-08-31 23:06:47 |
+----+----------+------------+----------------------------------+---------------------+
1 row in set (0.00 sec)2.6 走DBLE，模拟下业务的查询：
(testdb) > explain select t1.*,t2.* from order1 t1,order_detail t2 where t2.ord_status='1' and t2.id=1 and t1.id=t2.order_id;
+-----------------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| DATA_NODE | TYPE | SQL/REF |
+-----------------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| dn1_0 | BASE SQL | select `t2`.`id`,`t2`.`order_id`,`t2`.`ord_status`,`t2`.`address`,`t2`.`create_time`,`t1`.`id`,`t1`.`sn`,`t1`.`create_time` from `order1` `t1` join `order_detail` `t2` on `t1`.`id` = `t2`.`order_id` where (`t2`.`ord_status` = '1') AND (`t2`.`id` = 1) |
| dn2_0 | BASE SQL | select `t2`.`id`,`t2`.`order_id`,`t2`.`ord_status`,`t2`.`address`,`t2`.`create_time`,`t1`.`id`,`t1`.`sn`,`t1`.`create_time` from `order1` `t1` join `order_detail` `t2` on `t1`.`id` = `t2`.`order_id` where (`t2`.`ord_status` = '1') AND (`t2`.`id` = 1) |
| dn3_0 | BASE SQL | select `t2`.`id`,`t2`.`order_id`,`t2`.`ord_status`,`t2`.`address`,`t2`.`create_time`,`t1`.`id`,`t1`.`sn`,`t1`.`create_time` from `order1` `t1` join `order_detail` `t2` on `t1`.`id` = `t2`.`order_id` where (`t2`.`ord_status` = '1') AND (`t2`.`id` = 1) |
| merge_1 | MERGE | dn1_0; dn2_0; dn3_0 |
| shuffle_field_1 | SHUFFLE_FIELD | merge_1 |
+-----------------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows in set (0.00 sec)
(testdb) > SELECT
t1.*,
t2.*
FROM
order1 t1,
order_detail t2
WHERE t2.ord_status = '1'
AND t2.id = 1
AND t1.id = t2.order_id ;
+----+--------+---------------------+----+----------+------------+-----------------------------------+---------------------+
| id | sn | create_time | id | order_id | ord_status | address | create_time |
+----+--------+---------------------+----+----------+------------+-----------------------------------+---------------------+
| 1 | BJ0001 | 2019-08-31 23:05:36 | 1 | 1 | 1 | test data of ORDER1(ID=1,BJ001) | 2019-08-31 23:06:17 |
+----+--------+---------------------+----+----------+------------+-----------------------------------+---------------------+
1 row in set (0.00 sec)2.7 总结：当子表与父表的关联字段正好是父表的分片字段时，子表直接根据父表规则进行分片，在数据录入的时候子表直接放在父表的分片上面，在进行关联查询join的时候，走的是父表的路由。
【重要】其它的总结：
当子表与父表的关联字段不是父表的分片字段时，必须通过查找对应的父表记录来确认子表所在分片，如果找不到则会抛出错误，在join查询的时候，路由走的是所有分片节点。
本文参与 腾讯云自媒体分享计划，分享自作者个人站点/博客。
原始发表：2019-09-01 ，如有侵权请联系 cloudcommunity@tencent.com 删除
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