DBLE分庫分表實戰

• 2019 年 10 月 4 日
• 筆記

環境： 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-社區版-5.7.26

添加2個帳號授權：

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'@'%' ;

連接方式：

讀寫：

mysql -urw -prw123456 --port 8066 -h 192.168.20.10 testdb 

只讀：

mysql -urd -prd123456 --port 8066 -h 192.168.20.10 testdb 

ddl專用：

mysql -uop -p123456 --port 8066 -h 192.168.20.10 testdb 

管理帳號：

mysql -uman1 -p654321 --port 9066 -h 192.168.20.10 

解壓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   修改後的如下：

<?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分區2 -->          <table name="travelrecord" primaryKey="id" dataNode="dn1,dn2,dn3" rule="sharding-by-range_t"/>            <!-- hash mod 3 分區 -->          <table name="hotnews" primaryKey="id" dataNode="dn1,dn2,dn3" rule="id-sharding-by-mod3"/>            <!-- hashStringmod3 分區 -->          <table name="user_auth" primaryKey="open_id" dataNode="dn1,dn2,dn3" rule="user-auth-sharding-by-open_id" />            <!-- ER 分區 -->          <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 修改後的內容如下：

    <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><!-- 不符合條件的插入到第一個分區去 -->      </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位進行hash取模後再決定數據落在那個分片上 -->      </function>

[root@centos7 /usr/local/dble/conf ]#  vim autopartition-long_t.txt  # 增加一個路由規則文件

# range start-end ,data node index  # K=1000,M=10000.  # 範圍：前開後閉 (開區間，閉區間]  0-1M=0  1M-2M=1  2M-3M=2

vim server.xml 內容如下：

修改user部分為如下：       <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>

然後， reload 下 dble ， 進行測試

ddl專用：

mysql -uop -p123456 --port 8066 -h 192.168.20.10 testdb 

去創建符合上面的要求的幾個表,並寫入數據測試：

## 測試range分區  (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 |  +-------+---------+------------+--------+------+

## 測試全局表  (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)    多執行幾次，你會看到三個分片上都插入了3條數據，因為company定義為全局表。    (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 ;   命令也可以看到隨機分發到3個節點的。

## 測試hashmod分區  create table hotnews (id bigint unsigned not null primary key ,title varchar(400) ,created_time datetime) ENGINE=InnoDB DEFAULT CHARSET=utf8;    然後， 我們寫個腳本，批量插入些數據，看看情況：    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    然後，到後端的3個分片上看下數據量，大致如下，還是比較均勻的：  (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分區  CREATE TABLE `user_auth` (    `id` bigint unsigned NOT NULL AUTO_INCREMENT COMMENT '主鍵id',    `open_id` varchar(100) NOT NULL DEFAULT '' COMMENT '第三方授權id',    `union_id` varchar(100) NOT NULL DEFAULT '' COMMENT '授權的關聯id',    PRIMARY KEY (`id`)  ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='用戶AUTH資訊表' ;    #### 注意：實際生產環境的主鍵id需要由程式去保證唯一性（例如使用雪花演算法）    (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)

上面就是幾種常用的分區了， 另外還有種 date類型按時間分區的可能在日誌表的場景下也常用些。

date類型分區的實驗：

先去後端的db上創建物理的庫：

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 ;

修改後的 schema.xml 類似如下：

<?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>

然後，到 rule.xml中添加規則：

<tableRule name="sharding-by-month-user">      <rule>          <columns>addData</columns>          <algorithm>partbymonth-user</algorithm>      </rule>  </tableRule>    <!-- 加的基於月份的分片規則， 注意如果數量超了 會插入報錯 -->      <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>  <!-- 默認是每10天一個分片。我這裡改成每30天一個分片，另外注意並不按照固定的月來寫入 -->          <property name="defaultNode">0</property><!-- 默認小於 2018-01-01 的數據插入到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，我們需要提前人工把分片加好 並做好可用分區的監控，不然會造成無法寫入數據的事故出現。      (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)      查詢測試：  (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類型的可用分區的監控（腳本的原理同樣適用於其他類型的分區）：

簡單的做法就是定期執行一個explain的insert插入測試， 如果有ERROR關鍵字就告警出來

一個簡單的腳本如下：  # 提前60天預警    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 "當前可用分片數量處於安全狀態"  else      echo "需要加新的分片了"  fi

date類型加新的分片的方法： 

1、修改schema.xml 加上新的分片的配置資訊，修改後大致這樣：    <?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、重載配置文件  reload @@config_all ;      3、去後端創建對應的物理庫    create database userdb14;  .....這裡省略其它的建庫語句.......  create database userdb23;    4、通過dble再次下發下建表命令  create table if not exists user (addData date, dbname varchar(32),username varchar(32),province varchar(16),age int(3));      5、插入數據測試  (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

ER 表 （互聯網場景下用多表JOIN的不多，因此ER分片規則不太常用到，但是需要大致的了解）：

下面的內容大篇幅參考： https://blog.csdn.net/zhanglei_16/article/details/50779929

1：ER分片關係簡介

有一類業務，例如訂單（ORDER）跟訂單明細表（ORDER_DETAIL）,明細表會依賴訂單單，就是該會存在表的主從關係，

這類似業務的切分可以抽象出合適的切分規則，比如根據用戶ID切分,其它相關的表都依賴於用戶ID，再或者根據訂單ID進行切分，

總之部分業務總會可以抽象出父子關係的表。這類表適用於ER分片表，子表的記錄與所關聯的父表記錄存放在同一個數據分片上，

避免數據Join跨庫操作，以order與order_detail例子為例，schema.xml中定義合適的分片配置,order,order_detail 根據order_id

迕行數據切分，保證相同order_id的數據分到同一個分片上，在進行數據插入操作時，Mycat會獲取order所在的分片，

然後將order_detail也插入到order所在的分片

2：父表按照主鍵ID分片，字表的分片欄位與主表ID關聯，配置為ＥＲ分片

2.1：在schema.xml添加如下配置配置文件修改

<!-- ER 分區 -->  <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裡面設定分片規則：

    <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>

然後， reload 下 dble 

2.2 先建表， order 和 order_detail 表，有主外鍵關係

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 錄入數據：

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)

錄入數據，一組組錄入，涉及到外鍵關係： 

第一組北京的訂單

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());

第二組上海的訂單：

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)

第三組廣州的訂單：

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)

第四組 武漢的訂單，這裡故意將order_id設置成4，看看效果，是否隨id為4的廣州的那組分片：

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)

通過DBLE，查看下數據寫入的情況：

(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)

直連後端的db1，看下數據情況 (db2 和 db3 上面的數據查看，使用同樣的方法）；

((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，模擬下業務的查詢：

(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 總結：當子表與父表的關聯欄位正好是父表的分片欄位時，子表直接根據父表規則進行分片，在數據錄入的時候子表直接放在父表的分片上面，在進行關聯查詢join的時候，走的是父表的路由。

【重要】其它的總結：

當子表與父表的關聯欄位不是父表的分片欄位時，必須通過查找對應的父表記錄來確認子表所在分片，如果找不到則會拋出錯誤，在join查詢的時候，路由走的是所有分片節點。