DBLE分库分表实战

环境：DBLE 2.19.03.0

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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：





    
        
        

        
        

        
        

        
        

        
         
             
        
    
    
    
    
    
    
    
        select user()

vim rule.xml 修改后的内容如下：

    
        
            id
            rangeLong2
        
    

    
        
            id
            hashmod3
        
    

    
                                                                                                                              
            open_id
            hashStringmod3
        
    

    
        autopartition-long_t.txt
        0
    
    
    
        3
        1
    
    
    
        3
        1
        0:20

[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=2

vim server.xml 内容如下：

修改user部分为如下： 
    
        654321
        true
        
    
    
        123456
        testdb
    
        

    
        rw123456
        testdb
    
    
        rd123456
        testdb
        true

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：






    
        
        
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
        select user()

然后，到 rule.xml中添加规则：


    
        addData
        partbymonth-user
    



    
        yyyy-MM-dd
            2018-01-01
         
        30  
        0

(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"
fi

date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：





    	
        
        
    

    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    

    
        select user()
        
            
        
    



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 -> 2019-12-01

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ER 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

在rule.xml里面设定分片规则：

hashmod3

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 ;

+----+----------+------------+--------------------------------------+---------------------+

+----+----------+------------+--------------------------------------+---------------------+

| 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;

+----+----------+------------+----------------------------------+---------------------+

+----+----------+------------+----------------------------------+---------------------+

| 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 ;

+----+--------+---------------------+----+----------+------------+-----------------------------------+---------------------+

+----+--------+---------------------+----+----------+------------+-----------------------------------+---------------------+

| 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！！！！

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