1. mycat分库分表和读写分离
1.1. 分库分表实现
1.1.1. Mycat安装与配置详解
1.1.1.1. 下载地址
[mycat](https://github.com/InstallingB/Mycat2)
[mycat2](http://dl.mycat.org.cn/)
以1.6.7.1版本为例：
根据不同这里下载不同的版本（这里没有显示全，补过可以通过把鼠标移上去看左下角显示的链接信息）
因为我要下linux版本的。所以我下载第一个Mycat-server-1.6.7.1-release-20190213150257-lin的
1.1.1.2. 解压安装包
tar -xvf Mycat-server-1.6.7.1-release-20190213150257-linux.tar.gz
可以看到解压出来的内容都放到mycat文件夹中了。
目录说明：
- bin:命令文件
- catlet:空的,扩展
- conf:配置文件(server.xml,schema.xml,rule.xml等)
- lib:依赖的jar包
在mycat根目录创建logs文件夹，会把日志自动记录到这里。
如果启动报错可以到此目录查看日志
注意防火墙策略，并且添加/etc/hosts 中的主机名和IP的对应。
1.1.2. 修改mycat内存配置
我的linux虚拟机配置的内存大小是1GB，大家需要根据自己的实际情况进行配置
[root@centos212 local]# vi mycat/conf/wrapper.conf
wrapper.java.additional.10=-Xmx4G (大约在36行)
wrapper.java.additional.11=-Xms1G
改成
wrapper.java.additional.10=-Xmx1G
wrapper.java.additional.11=-Xms256M
添加环境变量
将MyCat配置到环境变量中
MYCAT_HOME=/usr/local/mycat
PATH=$MYCAT_HOME/bin:$PATH
1.1.3. 配置实例
1.1.4. 环境准备
两台虚拟机
- Host1 : 192.168.12.66
- Host2 : 192.168.12.88
- MySQL 5.7
- Mycat 1.6
1.1.4.1. 分库分表规则
mycat定义两个逻辑库，分别为db_user、db_store
表db_user按照模分成2个片,分别落在host1、host2上
表db_store为主从复制模式，分别落在host1、host2上
1.1.4.2. 创建数据库和表结构
分别在HOST1、HOST2执行db_user.sql
CREATE DATABASE db_user DEFAULT CHARACTER SET utf8 COLLATE utf8_general_ci;
use db_user;
-- ----------------------------
-- Table structure for data_dictionary
-- ----------------------------
DROP TABLE IF EXISTS `data_dictionary`;
CREATE TABLE `data_dictionary` (
`dataDictionaryID` int(11) NOT NULL COMMENT '数据字典ID',
`displayName` varchar(32) COLLATE utf8_bin DEFAULT NULL COMMENT '显示名称',
`value` varchar(255) COLLATE utf8_bin DEFAULT NULL COMMENT '数据字典取值',
`createTime` datetime DEFAULT NULL COMMENT '创建时间',
`lastUpdate` datetime DEFAULT NULL COMMENT '最后更新时间',
PRIMARY KEY (`dataDictionaryID`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
-- ----------------------------
-- Table structure for user_address
-- ----------------------------
DROP TABLE IF EXISTS `user_address`;
CREATE TABLE `user_address` (
`addressID` int(11) NOT NULL COMMENT '地址ID',
`receiver` varchar(16) COLLATE utf8_bin DEFAULT NULL COMMENT '收货人',
`addressDetail` varchar(32) COLLATE utf8_bin DEFAULT NULL COMMENT '地址详细',
`userID` int(11) NOT NULL COMMENT '用户ID',
`createTime` datetime DEFAULT NULL COMMENT '创建时间',
`lastUpdate` datetime DEFAULT NULL COMMENT '最后更新时间',
PRIMARY KEY (`addressID`,`userID`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
-- ----------------------------
-- Table structure for users
-- ----------------------------
DROP TABLE IF EXISTS `users`;
CREATE TABLE `users` (
`userID` int(11) NOT NULL COMMENT '用户ID',
`username` varchar(16) COLLATE utf8_bin DEFAULT NULL COMMENT '用户名',
`phoneNum` varchar(32) COLLATE utf8_bin DEFAULT NULL COMMENT '手机号码',
`age` int(11) DEFAULT NULL COMMENT '年龄',
`ddID` int(11) DEFAULT NULL COMMENT '所属会员类型',
`createTime` datetime DEFAULT NULL COMMENT '注册时间',
`lastUpdate` datetime DEFAULT NULL COMMENT '最后更新时间',
PRIMARY KEY (`userID`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
只需要在HOST1(192.168.12.66)上执行db_store.sql，HOST2主从复制会自动同步数据
CREATE DATABASE db_store DEFAULT CHARACTER SET utf8 COLLATE utf8_general_ci;
use db_store;
-- ----------------------------
-- Table structure for employee
-- ----------------------------
DROP TABLE IF EXISTS `employee`;
CREATE TABLE `employee` (
`employeeID` int(11) NOT NULL,
`userName` varchar(16) COLLATE utf8_bin DEFAULT NULL,
`phoneNum` varchar(32) COLLATE utf8_bin DEFAULT NULL,
`age` int(11) DEFAULT NULL,
`createTime` datetime DEFAULT NULL,
`lastUpdate` datetime DEFAULT NULL,
PRIMARY KEY (`employeeID`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_bin;
-- ----------------------------
-- Table structure for store
-- ----------------------------
DROP TABLE IF EXISTS `store`;
CREATE TABLE `store` (
`storeID` int(11) NOT NULL,
`storeName` varchar(32) COLLATE utf8_bin DEFAULT NULL,
`storeAddress` varchar(256) COLLATE utf8_bin DEFAULT NULL,
`createTime` datetime DEFAULT NULL,
`lastUpdate` datetime DEFAULT NULL,
PRIMARY KEY (`storeID`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_bin
执行完毕可以看到两个数据库下都对应生成了表。
1.1.4.3. 配置conf文件
两台host主机Mycat的conf目录下配置三个配置文件
schema.xml
<?xml version="1.0"?>
<!DOCTYPE mycat:schema SYSTEM "schema.dtd">
<mycat:schema xmlns:mycat="http://io.mycat/">
<schema name="db_store" checkSQLschema="false" sqlMaxLimit="100">
<table name="store" dataNode="db_store_dataNode" primaryKey="storeID"/>
<table name="employee" dataNode="db_store_dataNode" primaryKey="employeeID"/>
</schema>
<schema name="db_user" checkSQLschema="false" sqlMaxLimit="100">
<table name="data_dictionary" type="global" dataNode="db_user_dataNode1,db_user_dataNode2" primaryKey="dataDictionaryID"/>
<table name="users" dataNode="db_user_dataNode$1-2" rule="mod-userID-long" primaryKey="userID">
<childTable name="user_address" joinKey="userID" parentKey="userID" primaryKey="addressID"/>
</table>
</schema>
<!-- 节点配置 -->
<!-- db_store -->
<dataNode name="db_store_dataNode" dataHost="db_storeHOST" database="db_store" />
<!-- db_user -->
<dataNode name="db_user_dataNode1" dataHost="db_userHOST1" database="db_user" />
<dataNode name="db_user_dataNode2" dataHost="db_userHOST2" database="db_user" />
<!-- 节点主机配置 -->
<!-- 配置db_store的节点主机 -->
<dataHost name="db_storeHOST" maxCon="1000" minCon="10" balance="1"
writeType="0" dbType="mysql" dbDriver="native" switchType="1" slaveThreshold="100">
<heartbeat>select user()</heartbeat>
<!-- can have multi write hosts -->
<writeHost host="hostM1" url="192.168.12.66:3306" user="root" password="123456">
<!-- can have multi read hosts -->
<readHost host="hostS1" url="192.168.12.88:3306" user="root" password="123456" />
</writeHost>
</dataHost>
<!-- 配置db_user的节点主机 -->
<dataHost name="db_userHOST1" maxCon="1000" minCon="10" balance="0"
writeType="0" dbType="mysql" dbDriver="native" switchType="1" slaveThreshold="100">
<heartbeat>select user()</heartbeat>
<writeHost host="userHost1" url="192.168.12.66:3306" user="root" password="123456">
</writeHost>
</dataHost>
<dataHost name="db_userHOST2" maxCon="1000" minCon="10" balance="0"
writeType="0" dbType="mysql" dbDriver="native" switchType="1" slaveThreshold="100">
<heartbeat>select user()</heartbeat>
<!-- can have multi write hosts -->
<writeHost host="userHost2" url="192.168.12.88:3306" user="root" password="123456">
</writeHost>
</dataHost>
</mycat:schema>
server.xml
<?xml version="1.0" encoding="UTF-8"?>
<!-- - - Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License. - You
may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0
- - Unless required by applicable law or agreed to in writing, software -
distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the
License for the specific language governing permissions and - limitations
under the License. -->
<!DOCTYPE mycat:server SYSTEM "server.dtd">
<mycat:server xmlns:mycat="http://io.mycat/">
<system>
<property name="useSqlStat">0</property> <!-- 1为开启实时统计、0为关闭 -->
<property name="useGlobleTableCheck">0</property> <!-- 1为开启全加班一致性检测、0为关闭 -->
<property name="sequnceHandlerType">2</property>
<!-- <property name="useCompression">1</property>--> <!--1为开启mysql压缩协议-->
<!-- <property name="fakeMySQLVersion">5.6.20</property>--> <!--设置模拟的MySQL版本号-->
<!-- <property name="processorBufferChunk">40960</property> -->
<!--
<property name="processors">1</property>
<property name="processorExecutor">32</property>
-->
<!--默认为type 0: DirectByteBufferPool | type 1 ByteBufferArena-->
<property name="processorBufferPoolType">0</property>
<!--默认是65535 64K 用于sql解析时最大文本长度 -->
<!--<property name="maxStringLiteralLength">65535</property>-->
<!--<property name="sequnceHandlerType">0</property>-->
<!--<property name="backSocketNoDelay">1</property>-->
<!--<property name="frontSocketNoDelay">1</property>-->
<!--<property name="processorExecutor">16</property>-->
<!--
<property name="serverPort">8066</property> <property name="managerPort">9066</property>
<property name="idleTimeout">300000</property> <property name="bindIp">0.0.0.0</property>
<property name="frontWriteQueueSize">4096</property> <property name="processors">32</property> -->
<!--分布式事务开关，0为不过滤分布式事务，1为过滤分布式事务（如果分布式事务内只涉及全局表，则不过滤），2为不过滤分布式事务,但是记录分布式事务日志-->
<property name="handleDistributedTransactions">0</property>
<!--
off heap for merge/order/group/limit 1开启 0关闭
-->
<property name="useOffHeapForMerge">1</property>
<!--
单位为m
-->
<property name="memoryPageSize">1m</property>
<!--
单位为k
-->
<property name="spillsFileBufferSize">1k</property>
<property name="useStreamOutput">0</property>
<!--
单位为m
-->
<property name="systemReserveMemorySize">384m</property>
<!--是否采用zookeeper协调切换 -->
<property name="useZKSwitch">true</property>
</system>
<!-- 全局SQL防火墙设置 -->
<!--
<firewall>
<whitehost>
<host host="127.0.0.1" user="mycat"/>
<host host="127.0.0.2" user="mycat"/>
</whitehost>
<blacklist check="false">
</blacklist>
</firewall>
-->
<user name="root">
<property name="password">123456</property>
<property name="schemas">db_store,db_user</property>
<!-- 表级 DML 权限设置 -->
<!--
<privileges check="false">
<schema name="db_user" dml="0110" >
<table name="users" dml="1111"></table> IUSD
<table name="useraddres" dml="1110"></table>
</schema>
</privileges>
-->
</user>
</mycat:server>
rule.xml
<?xml version="1.0" encoding="UTF-8"?>
<!-- - - Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License. - You
may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0
- - Unless required by applicable law or agreed to in writing, software -
distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the
License for the specific language governing permissions and - limitations
under the License. -->
<!DOCTYPE mycat:rule SYSTEM "rule.dtd">
<mycat:rule xmlns:mycat="http://io.mycat/">
<tableRule name="rule1">
<rule>
<columns>id</columns>
<algorithm>func1</algorithm>
</rule>
</tableRule>
<tableRule name="rule2">
<rule>
<columns>user_id</columns>
<algorithm>func1</algorithm>
</rule>
</tableRule>
<tableRule name="sharding-by-intfile">
<rule>
<columns>sharding_id</columns>
<algorithm>hash-int</algorithm>
</rule>
</tableRule>
<tableRule name="auto-sharding-long">
<rule>
<columns>id</columns>
<algorithm>rang-long</algorithm>
</rule>
</tableRule>
<tableRule name="mod-long">
<rule>
<columns>id</columns>
<algorithm>mod-long</algorithm>
</rule>
</tableRule>
<tableRule name="mod-userID-long">
<rule>
<columns>userID</columns>
<algorithm>mod-long</algorithm>
</rule>
</tableRule>
<tableRule name="sharding-by-murmur">
<rule>
<columns>id</columns>
<algorithm>murmur</algorithm>
</rule>
</tableRule>
<tableRule name="crc32slot">
<rule>
<columns>id</columns>
<algorithm>crc32slot</algorithm>
</rule>
</tableRule>
<tableRule name="sharding-by-month">
<rule>
<columns>create_time</columns>
<algorithm>partbymonth</algorithm>
</rule>
</tableRule>
<tableRule name="latest-month-calldate">
<rule>
<columns>calldate</columns>
<algorithm>latestMonth</algorithm>
</rule>
</tableRule>
<tableRule name="auto-sharding-rang-mod">
<rule>
<columns>id</columns>
<algorithm>rang-mod</algorithm>
</rule>
</tableRule>
<tableRule name="jch">
<rule>
<columns>id</columns>
<algorithm>jump-consistent-hash</algorithm>
</rule>
</tableRule>
<function name="mod-long" class="io.mycat.route.function.PartitionByMod">
<!-- how many data nodes -->
<property name="count">2</property>
</function>
<function name="murmur"
class="io.mycat.route.function.PartitionByMurmurHash">
<property name="seed">0</property><!-- 默认是0 -->
<property name="count">2</property><!-- 要分片的数据库节点数量，必须指定，否则没法分片 -->
<property name="virtualBucketTimes">160</property><!-- 一个实际的数据库节点被映射为这么多虚拟节点，默认是160倍，也就是虚拟节点数是物理节点数的160倍 -->
<!-- <property name="weightMapFile">weightMapFile</property> 节点的权重，没有指定权重的节点默认是1。以properties文件的格式填写，以从0开始到count-1的整数值也就是节点索引为key，以节点权重值为值。所有权重值必须是正整数，否则以1代替 -->
<!-- <property name="bucketMapPath">/etc/mycat/bucketMapPath</property>
用于测试时观察各物理节点与虚拟节点的分布情况，如果指定了这个属性，会把虚拟节点的murmur hash值与物理节点的映射按行输出到这个文件，没有默认值，如果不指定，就不会输出任何东西 -->
</function>
<function name="crc32slot"
class="io.mycat.route.function.PartitionByCRC32PreSlot">
<property name="count">2</property><!-- 要分片的数据库节点数量，必须指定，否则没法分片 -->
</function>
<function name="hash-int"
class="io.mycat.route.function.PartitionByFileMap">
<property name="mapFile">partition-hash-int.txt</property>
</function>
<function name="rang-long"
class="io.mycat.route.function.AutoPartitionByLong">
<property name="mapFile">autopartition-long.txt</property>
</function>
<function name="func1" class="io.mycat.route.function.PartitionByLong">
<property name="partitionCount">8</property>
<property name="partitionLength">128</property>
</function>
<function name="latestMonth"
class="io.mycat.route.function.LatestMonthPartion">
<property name="splitOneDay">24</property>
</function>
<function name="partbymonth"
class="io.mycat.route.function.PartitionByMonth">
<property name="dateFormat">yyyy-MM-dd</property>
<property name="sBeginDate">2015-01-01</property>
</function>
<function name="rang-mod" class="io.mycat.route.function.PartitionByRangeMod">
<property name="mapFile">partition-range-mod.txt</property>
</function>
<function name="jump-consistent-hash" class="io.mycat.route.function.PartitionByJumpConsistentHash">
<property name="totalBuckets">3</property>
</function>
</mycat:rule>
1.1.4.4. 重启mycat
[root@localhost bin]# pwd
/software/mycat/bin
[root@localhost bin]# ./mycat restart
Stopping Mycat-server...
Mycat-server was not running.
Starting Mycat-server...
[root@localhost bin]# ./mycat status
Mycat-server is running (8228).
注意mycat是java编写的，因此需要安装jdk,此处省略
1.1.4.5. 连接mycat
linux 控制台 mysql -uroot -padmin -P8066 -h192.168.12.66
查看逻辑库show database;
只能看到逻辑库 db_store、db_user
1.1.4.6. 插入数据测试
分片表users
通过mycat连接db_user库，执行如下SQL
insertdb_user_users.sql
INSERT INTO `users`(userID,userName,phoneNum,age,ddID,createTime,lastUpdate) VALUES ('1', '张1', '13611111111', '31', '2', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `users`(userID,userName,phoneNum,age,ddID,createTime,lastUpdate) VALUES ('2', '王二', '13622222222', '32', '5', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `users`(userID,userName,phoneNum,age,ddID,createTime,lastUpdate) VALUES ('3', '李三', '13633333333', '33', '3', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `users`(userID,userName,phoneNum,age,ddID,createTime,lastUpdate) VALUES ('4', '赵四', '13644444444', '34', '1', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `users`(userID,userName,phoneNum,age,ddID,createTime,lastUpdate) VALUES ('5', '田五', '13655555555', '35', '3', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
ER表user_address
insertdb_user_user_address.sql
INSERT INTO `user_address`(addressID,receiver,addressDetail,userID,createTime,lastUPdate) VALUE ('1', '张一', '深圳南山科技园', '1', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `user_address`(addressID,receiver,addressDetail,userID,createTime,lastUPdate) VALUE ('2', '张一', '深圳龙华地铁站', '1', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `user_address`(addressID,receiver,addressDetail,userID,createTime,lastUPdate) VALUE ('3', '王二', '长沙麓谷软件园', '2', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `user_address`(addressID,receiver,addressDetail,userID,createTime,lastUPdate) VALUE ('4', '赵四', '长沙麓谷企业广场', '4', '2018-10-10 13:46:36', '2018-10-10 13:46:36');
INSERT INTO `user_address`(addressID,receiver,addressDetail,userID,createTime,lastUPdate) VALUE ('5', '李三', '深圳福田侨香', '3', '2018-10-10 13:46:36', '2018-10-10 13:46:36');
全局表data_dictionary
mycat上插入数据
INSERT INTO `data_dictionary`(dataDictionaryID,displayName,value,createTime,lastUpdate) VALUE ('1', '白银', 'BY', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `data_dictionary`(dataDictionaryID,displayName,value,createTime,lastUpdate) VALUE ('2', '黄金', 'HJ', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `data_dictionary`(dataDictionaryID,displayName,value,createTime,lastUpdate) VALUE ('3', '砖石', 'ZS', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `data_dictionary`(dataDictionaryID,displayName,value,createTime,lastUpdate) VALUE ('4', '大师', 'DS', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
INSERT INTO `data_dictionary`(dataDictionaryID,displayName,value,createTime,lastUpdate) VALUE ('5', '王者', 'WZ', '2018-10-10 13:39:41', '2018-10-10 13:39:41');
全局表每个节点的数据都一样
db_store测试
insert_db_store_store.sql
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (1, '深圳宝安区圣淘沙店', '宝安区圣淘沙骏园5栋18号商铺', '20170413101010', '20170413101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (2, '深圳罗湖区红宝路1店', '罗湖区红宝路112号', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (3, '深圳福田区梅华路店', '福田区上梅林梅华路上梅林菜场斜对面', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (4, '深圳福田区景田店', '福田区景田西住宅区七栋首层(民润超对面)', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (5, '深圳宝安区富通苑店', '宝安区新安街道48区富通苑A栋115号', '20170413101010', '20170413101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (6, '深圳罗湖区海富花园店', '罗湖区深南东路海富花园福怡阁底层A8铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (7, '深圳龙岗区四季花城店', '龙岗区坂田四季花城B19号商铺', '20180131101010', '20180131101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (8, '深圳南山区蔚蓝海岸店', '南山区登良路蔚蓝海岸商铺B09号', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (9, '深圳福田区中康路店', '福田区上梅林中康路梅林医院斜对面振业梅苑B栋110号', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (10, '深圳罗湖区松泉山庄店', '罗湖区太白路松泉山庄4栋6栋楼裙碧涟阁首层商场103-3号铺', '20170330101010', '20170330101010');
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INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (12, '深圳宝安区桃源居3店', '宝安区前进路桃源居48区', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (13, '深圳南山区学府店', '南山区后海大道西宏观苑商铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (14, '深圳福田区车公庙店', '福田区泰然工贸园四路105栋首层(泰康轩旁)', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (15, '深圳福田区金地店', '福田区金地一路金海丽名居102号商铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (16, '深圳罗湖区龙园山庄店', '罗湖区龙园山庄1栋102A', '20170330101010', '20170330101010');
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INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (18, '深圳宝安区三联路店', '宝安区龙华镇三联路181号一楼右层', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (19, '深圳宝安区深业新岸线店', '宝安区深业新岸线2栋11号铺', '20170413101010', '20170413101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (20, '深圳宝安区风和日丽店', '宝安区龙华镇丰润花园13栋21号铺', '20171016101010', '20171016101010');
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INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (22, '深圳南山区西海湾店', '南山区南商路西海湾花园单身公寓A4号商铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (23, '深圳南山区招商海月店', '南山区后海路招商海月花园24栋1-1', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (24, '深圳罗湖区布心店', '罗湖区布心村太白路1号', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (25, '深圳宝安区美丽365花园店', '宝安区龙华镇东环一路美丽365花园B2栋102商铺', '20170330101010', '20170330101010');
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INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (27, '深圳宝安区天骄店', '宝安区天骄世家花园125#商铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (28, '深圳宝安区东源阁店', '宝安区龙华镇东环二路东源阁C区一栋112商铺', '20170330101010', '20170330101010');
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INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (78, '滨海之窗店(停)', NULL, '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (79, '深圳宝安区滢水山庄店', '宝安区滢水山庄二区8栋106', '20170330101010', '20170330101010');
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INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (82, '百乐汇店中店', NULL, '20170330101010', '20170330101010');
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INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (87, '测试门店1', NULL, '20180926101010', '20180926101010');
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INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (94, '深圳宝安区龙泉花园店', '宝安区龙华龙泉花园A14号铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (95, '深圳罗湖区中兴路店', '罗湖区中兴路103号商铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (96, '深圳龙岗区东大街2店', '龙岗区布吉东大街桂芳园7区29栋D102铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (97, '深圳罗湖区莲塘聚福路店', '罗湖区莲塘聚福路鹏兴花园56栋114铺', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (98, '深圳南山区东滨路店', '南山区东滨路蛇口兰园大厦15A', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (99, '深圳福田区国都高尔夫花园店', '福田区新沙路国都高尔夫花园翠逸阁1层107C号', '20170330101010', '20170330101010');
INSERT INTO `store` (`storeID`, `storeName`, `storeAddress`, `createTime`, `lastUpdate`) VALUES (100, '东莞市中信凯旋城', '东莞市东城区火炼树怡丰都市广场怡祥阁B2铺', '20180223180232', '20180223180232');
1.1.5. 配置详解
1.1.5.1. rule规则详解
常用的分片规则：总共十个（基本够用）
一、枚举法
<tableRule name="sharding-by-intfile">
<rule>
<columns>user_id</columns>
<algorithm>hash-int</algorithm>
</rule>
</tableRule>
<function name="hash-int" class="io.mycat.route.function.PartitionByFileMap">
<property name="mapFile">partition-hash-int.txt</property>
<property name="type">0</property>
<property name="defaultNode">0</property>
</function>
partition-hash-int.txt 配置：
10000=0
10010=1
上面columns 标识将要分片的表字段，algorithm 分片函数，
其中分片函数配置中，mapFile标识配置文件名称，type默认值为0，0表示Integer，非零表示String，
所有的节点配置都是从0开始，及0代表节点1
/**
* defaultNode 默认节点:小于0表示不设置默认节点，大于等于0表示设置默认节点,结点为指定的值
*
默认节点的作用：枚举分片时，如果碰到不识别的枚举值，就让它路由到默认节点
* 如果不配置默认节点（defaultNode值小于0表示不配置默认节点），碰到
* 不识别的枚举值就会报错，
* like this：can't find datanode for sharding column:column_name val:ffffffff
*/
二、固定分片hash算法
<tableRule name="rule1">
<rule>
<columns>user_id</columns>
<algorithm>func1</algorithm>
</rule>
</tableRule>
<function name="func1" class="io.mycat.route.function.PartitionByLong">
<property name="partitionCount">2,1</property>
<property name="partitionLength">256,512</property>
</function>
配置说明：
上面columns 标识将要分片的表字段，algorithm 分片函数，
partitionCount 分片个数列表，partitionLength 分片范围列表
分区长度:默认为最大2^n=1024 ,即最大支持1024分区
约束 :
count,length两个数组的长度必须是一致的。
1024 = sum((count[i]*length[i])). count和length两个向量的点积恒等于1024
用法例子：
@Test
public void testPartition() {
// 本例的分区策略：希望将数据水平分成3份，前两份各占25%，第三份占50%。（故本例非均匀分区）
// |<---------------------1024------------------------>|
// |<----256--->|<----256--->|<----------512---------->|
// | partition0 | partition1 | partition2 |
// | 共2份,故count[0]=2 | 共1份，故count[1]=1 |
int[] count = new int[] { 2, 1 };
int[] length = new int[] { 256, 512 };
PartitionUtil pu = new PartitionUtil(count, length);
// 下面代码演示分别以offerId字段或memberId字段根据上述分区策略拆分的分配结果
int DEFAULT_STR_HEAD_LEN = 8; // cobar默认会配置为此值
long offerId = 12345;
String memberId = "qiushuo";
// 若根据offerId分配，partNo1将等于0，即按照上述分区策略，offerId为12345时将会被分配到partition0中
int partNo1 = pu.partition(offerId);
// 若根据memberId分配，partNo2将等于2，即按照上述分区策略，memberId为qiushuo时将会被分到partition2中
int partNo2 = pu.partition(memberId, 0, DEFAULT_STR_HEAD_LEN);
Assert.assertEquals(0, partNo1);
Assert.assertEquals(2, partNo2);
}
如果需要平均分配设置：平均分为4分片，partitionCount*partitionLength=1024
<function name="func1" class="org.opencloudb.route.function.PartitionByLong">
<property name="partitionCount">4</property>
<property name="partitionLength">256</property>
</function>
三、范围约定
<tableRule name="auto-sharding-long">
<rule>
<columns>user_id</columns>
<algorithm>rang-long</algorithm>
</rule>
</tableRule>
<function name="rang-long" class="io.mycat.route.function.AutoPartitionByLong">
<property name="mapFile">autopartition-long.txt</property>
</function>
# range start-end ,data node index
# K=1000,M=10000.
0-500M=0
500M-1000M=1
1000M-1500M=2
或
0-10000000=0
10000001-20000000=1
配置说明：
上面columns 标识将要分片的表字段，algorithm 分片函数，
rang-long 函数中mapFile代表配置文件路径
所有的节点配置都是从0开始，及0代表节点1，此配置非常简单，即预先制定可能的id范围到某个分片
四、求模法
<tableRule name="mod-long">
<rule>
<columns>user_id</columns>
<algorithm>mod-long</algorithm>
</rule>
</tableRule>
<function name="mod-long" class="io.mycat.route.function.PartitionByMod">
<!-- how many data nodes -->
<property name="count">3</property>
</function>
配置说明：
上面columns 标识将要分片的表字段，algorithm 分片函数，
此种配置非常明确即根据id与count（你的结点数）进行求模预算，相比方式1，此种在批量插入时需要切换数据源，id不连续
五、日期列分区法
<tableRule name="sharding-by-date">
<rule>
<columns>create_time</columns>
<algorithm>sharding-by-date</algorithm>
</rule>
</tableRule>
<function name="sharding-by-date" class="io.mycat.route.function..PartitionByDate">
<property name="dateFormat">yyyy-MM-dd</property>
<property name="sBeginDate">2014-01-01</property>
<property name="sPartionDay">10</property>
</function>
配置说明：
上面columns 标识将要分片的表字段，algorithm 分片函数，
配置中配置了开始日期，分区天数，即默认从开始日期算起，分隔10天一个分区
还有一切特性请看源码
Assert.assertEquals(true, 0 == partition.calculate("2014-01-01"));
Assert.assertEquals(true, 0 == partition.calculate("2014-01-10"));
Assert.assertEquals(true, 1 == partition.calculate("2014-01-11"));
Assert.assertEquals(true, 12 == partition.calculate("2014-05-01"));
六、通配取模
<tableRule name="sharding-by-pattern">
<rule>
<columns>user_id</columns>
<algorithm>sharding-by-pattern</algorithm>
</rule>
</tableRule>
<function name="sharding-by-pattern" class="io.mycat.route.function.PartitionByPattern">
<property name="patternValue">256</property>
<property name="defaultNode">2</property>
<property name="mapFile">partition-pattern.txt</property>
</function>
partition-pattern.txt
# id partition range start-end ,data node index
###### first host configuration
1-32=0
33-64=1
65-96=2
97-128=3
######## second host configuration
129-160=4
161-192=5
193-224=6
225-256=7
0-0=7
配置说明：
上面columns 标识将要分片的表字段，algorithm 分片函数，patternValue 即求模基数，defaoultNode 默认节点，如果不配置了默认，则默认是0即第一个结点
mapFile 配置文件路径
配置文件中，1-32 即代表id%256后分布的范围，如果在1-32则在分区1，其他类推，如果id非数字数据，则会分配在defaoultNode 默认节点
String idVal = "0";
Assert.assertEquals(true, 7 == autoPartition.calculate(idVal));
idVal = "45a";
Assert.assertEquals(true, 2 == autoPartition.calculate(idVal));
七、ASCII码求模通配
<tableRule name="sharding-by-prefixpattern">
<rule>
<columns>user_id</columns>
<algorithm>sharding-by-prefixpattern</algorithm>
</rule>
</tableRule>
<function name="sharding-by-pattern" class="io.mycat.route.function.PartitionByPrefixPattern">
<property name="patternValue">256</property>
<property name="prefixLength">5</property>
<property name="mapFile">partition-pattern.txt</property>
</function>
partition-pattern.txt
# range start-end ,data node index
# ASCII
# 48-57=0-9
# 64、65-90=@、A-Z
# 97-122=a-z
###### first host configuration
1-4=0
5-8=1
9-12=2
13-16=3
###### second host configuration
17-20=4
21-24=5
25-28=6
29-32=7
0-0=7
配置说明：
上面columns 标识将要分片的表字段，algorithm 分片函数，patternValue 即求模基数，prefixLength ASCII 截取的位数
mapFile 配置文件路径
配置文件中，1-32 即代表id%256后分布的范围，如果在1-32则在分区1，其他类推
此种方式类似方式6只不过采取的是将列种获取前prefixLength位列所有ASCII码的和进行求模sum%patternValue ,获取的值，在通配范围内的
即 分片数，
/**
* ASCII编码：
* 48-57=0-9阿拉伯数字
* 64、65-90=@、A-Z
* 97-122=a-z
*
*/
如
String idVal="gf89f9a";
Assert.assertEquals(true, 0==autoPartition.calculate(idVal));
idVal="8df99a";
Assert.assertEquals(true, 4==autoPartition.calculate(idVal));
idVal="8dhdf99a";
Assert.assertEquals(true, 3==autoPartition.calculate(idVal));
八、编程指定
<tableRule name="sharding-by-substring">
<rule>
<columns>user_id</columns>
<algorithm>sharding-by-substring</algorithm>
</rule>
</tableRule>
<function name="sharding-by-substring" class="io.mycat.route.function.PartitionDirectBySubString">
<property name="startIndex">0</property> <!-- zero-based -->
<property name="size">2</property>
<property name="partitionCount">8</property>
<property name="defaultPartition">0</property>
</function>
配置说明：
上面columns 标识将要分片的表字段，algorithm 分片函数
此方法为直接根据字符子串（必须是数字）计算分区号（由应用传递参数，显式指定分区号）。
例如id=05-100000002
在此配置中代表根据id中从startIndex=0，开始，截取siz=2位数字即05，05就是获取的分区，如果没传默认分配到defaultPartition
九、字符串拆分hash解析
<tableRule name="sharding-by-stringhash">
<rule>
<columns>user_id</columns>
<algorithm>sharding-by-stringhash</algorithm>
</rule>
</tableRule>
<function name="sharding-by-substring" class="io.mycat.route.function.PartitionByString">
<property name=length>512</property> <!-- zero-based -->
<property name="count">2</property>
<property name="hashSlice">0:2</property>
</function>
配置说明：
上面columns 标识将要分片的表字段，algorithm 分片函数
函数中length代表字符串hash求模基数，count分区数，hashSlice hash预算位
即根据子字符串 hash运算
hashSlice ： 0 means str.length(), -1 means str.length()-1
/**
* "2" -> (0,2)<br/>
* "1:2" -> (1,2)<br/>
* "1:" -> (1,0)<br/>
* "-1:" -> (-1,0)<br/>
* ":-1" -> (0,-1)<br/>
* ":" -> (0,0)<br/>
*/
public class PartitionByStringTest {
@Test
public void test() {
PartitionByString rule = new PartitionByString();
String idVal=null;
rule.setPartitionLength("512");
rule.setPartitionCount("2");
rule.init();
rule.setHashSlice("0:2");
// idVal = "0";
// Assert.assertEquals(true, 0 == rule.calculate(idVal));
// idVal = "45a";
// Assert.assertEquals(true, 1 == rule.calculate(idVal));
//last 4
rule = new PartitionByString();
rule.setPartitionLength("512");
rule.setPartitionCount("2");
rule.init();
//last 4 characters
rule.setHashSlice("-4:0");
idVal = "aaaabbb0000";
Assert.assertEquals(true, 0 == rule.calculate(idVal));
idVal = "aaaabbb2359";
Assert.assertEquals(true, 0 == rule.calculate(idVal));
}
十、一致性hash
<tableRule name="sharding-by-murmur">
<rule>
<columns>user_id</columns>
<algorithm>murmur</algorithm>
</rule>
</tableRule>
<function name="murmur" class="io.mycat.route.function.PartitionByMurmurHash">
<property name="seed">0</property><!-- 默认是0-->
<property name="count">2</property><!-- 要分片的数据库节点数量，必须指定，否则没法分片—>
<property name="virtualBucketTimes">160</property><!-- 一个实际的数据库节点被映射为这么多虚拟节点，默认是160倍，也就是虚拟节点数是物理节点数的160倍-->
<!--
<property name="weightMapFile">weightMapFile</property>
节点的权重，没有指定权重的节点默认是1。以properties文件的格式填写，以从0开始到count-1的整数值也就是节点索引为key，以节点权重值为值。所有权重值必须是正整数，否则以1代替 -->
<!--
<property name="bucketMapPath">/etc/mycat/bucketMapPath</property>
用于测试时观察各物理节点与虚拟节点的分布情况，如果指定了这个属性，会把虚拟节点的murmur hash值与物理节点的映射按行输出到这个文件，没有默认值，如果不指定，就不会输出任何东西 -->
</function>
1.1.5.2. schema.xml
<?xml version="1.0"?>
<!DOCTYPE mycat:schema SYSTEM "schema.dtd">
<mycat:schema xmlns:mycat="http://io.mycat/">
<!--
name：为mycat逻辑库的名字，对应server<property name="schemas">mydatabase</property>，
建议设置跟数据库一样的名称
checkSQLschema：自动检查逻辑库名称并拼接，true会在sql语句中的表名前拼接逻辑库名，
例如select * from mydatabase.t_user;
sqlMaxLimit：查询保护、如果没有写limit条件，会自动拼接。只查询100条。
-->
<schema name="mydatabase" checkSQLschema="true" sqlMaxLimit="100">
<!--
name:为物理数据库的表名，命名与物理数据库的一致
dataNode:为dataNode标签(<dataNode name="dn1" dataHost="dtHost1" database="db1" />)里面的name值
dataNode里面填写的节点数量必须和rule里面的规则数量一致
例如rule里面只定义了两个0-1M=0 1M-2M=1那么此处只可以指定两个节点,1M=10000，M为单位万
primaryKey:为表的ID字段，建议和rule.xml里面指定的ID和物理库的ID一致
rule：分片规则，对应rule.xml中<tableRule name="student_id">的name
type：表格类型，默认非global，用于全局表定义
-->
<table name="t_user" dataNode="dn1,dn2,dn3" primaryKey="id" rule="auto-sharding-long">
<!--ER分片注意childTable 标签需要放到table标签内，是主外键关联关系，
name:为物理数据库的表名，命名与物理数据库的一致
primaryKey:为表t_loginlog的ID字段，建议和rule.xml里面指定的ID和物理库的ID一致.
joinKey：从表t_loginlog的外键字段，需要和物理库的字段名称一致
parentKey：为主表t_user的字段名，依据此字段做关联，进行ER分片
-->
<childTable name="t_loginlog" primaryKey="id" joinKey="user_id" parentKey="id"></childTable>
</table>
<table name="t_student" dataNode="dn1,dn3" primaryKey="id" rule="student_id" />
<table name="t_dictionaries" dataNode="dn1,dn2,dn3" type="global" />
<table name="t_teacher" dataNode="dn1" />
</schema>
<!-- name：节点名称，用于在table标签里面调用
dataHost:dataHost标签name值(<dataHost name="dtHost1">)
database:物理数据库名，需要提前创建好实际存在的-->
<dataNode name="dn1" dataHost="dtHost1" database="db1" />
<dataNode name="dn2" dataHost="dtHost1" database="db2" />
<dataNode name="dn3" dataHost="dtHost2" database="db3" />
<!--
name：节点名称，在上方dataNode标签中调用
maxCon:底层数据库的链接最大数
minCon:底层数据库的链接最小数
balance:值可以为0,1,2,3,分别表示对当前datahost中维护的数据库们的读操作逻辑
0:不开启读写分离，所有的读写操作都在最小的索引号的writeHost(第一个writeHost标签)
1：全部的readHost和备用writeHost都参与读数据的平衡，如果读的请求过多，负责写的第一个writeHost也分担一部分
2 ：所有的读操作，都随机的在所有的writeHost和readHost中进行
3 ：所有的读操作，都到writeHost对应的readHost上进行（备用writeHost不参加了）,在集群中没有配置ReadHost的情况下,读都到第
一个writeHost完成
writeType:控制当前datahost维护的数据库集群的写操作
0：所有的写操作都在第一个writeHost标签的数据库进行
1：所有的写操作，都随机分配到所有的writeHost（mycat1.5完全不建议配置了）
dbtype：数据库类型（不同数据库配置不同名称，mysql）
dbDriver:数据库驱动，native,动态获取
switchType：切换的逻辑
-1：故障不切换
1：故障切换，当前写操作的writeHost故障，进行切换，切换到下一个writeHost；
slaveThreshold：标签中的<heartbeat>用来检测后端数据库的心跳sql语句;本属性检查从节点与主节点的同步情况(延迟时间数),配合心
跳语句show slave status; 读写分离时,所有的readHost的数据都可靠
-->
<dataHost name="dtHost1" maxCon="1000" minCon="10" balance="1"
writeType="0" dbType="mysql" dbDriver="native" switchType="1" slaveThreshold="100">
<!--用于验证心跳，这个是mysql主库的配置-->
<heartbeat>select user()</heartbeat>
<writeHost host="127.0.0.1" url="192.168.199.11:3306" user="root" password="123456">
<readHost host="127.0.0.1" url="192.168.199.12:3306" user="root" password="123456" />
</writeHost>
</dataHost>
<dataHost name="dtHost2" maxCon="1000" minCon="10" balance="1"
writeType="0" dbType="mysql" dbDriver="native" switchType="1" slaveThreshold="100">
<!--用于验证心跳，这个是mysql主库的配置-->
<heartbeat>select user()</heartbeat>
<writeHost host="127.0.0.1" url="192.168.199.13:3306" user="root" password="123456">
<readHost host="127.0.0.1" url="192.168.199.13:3306" user="root" password="123456" />
</writeHost>
</dataHost>
</mycat:schema>
1.1.5.3. server.xml详解
<?xml version="1.0" encoding="UTF-8"?>
<!-- - - Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License. - You
may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0
- - Unless required by applicable law or agreed to in writing, software -
distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the
License for the specific language governing permissions and - limitations
under the License. -->
<!DOCTYPE mycat:server SYSTEM "server.dtd">
<mycat:server xmlns:mycat="http://io.mycat/">
<system>
<!--配置的端口号-->
<property name="serverPort">8066</property>
<property name="nonePasswordLogin">0</property> <!-- 0为需要密码登陆、1为不需要密码登陆 ,默认为0，设置为1则需要指定默认账户-->
<property name="useHandshakeV10">1</property>
<property name="useSqlStat">0</property> <!-- 1为开启实时统计、0为关闭 -->
<property name="useGlobleTableCheck">0</property> <!-- 1为开启全加班一致性检测、0为关闭 -->
<property name="sequnceHandlerType">2</property>
<property name="subqueryRelationshipCheck">false</property> <!-- 子查询中存在关联查询的情况下,检查关联字段中是否有分片字段 .默认 false -->
<!-- <property name="useCompression">1</property>--> <!--1为开启mysql压缩协议-->
<!-- <property name="fakeMySQLVersion">5.6.20</property>--> <!--设置模拟的MySQL版本号-->
<!-- <property name="processorBufferChunk">40960</property> -->
<!--
<property name="processors">1</property>
<property name="processorExecutor">32</property>
-->
<!--默认为type 0: DirectByteBufferPool | type 1 ByteBufferArena | type 2 NettyBufferPool -->
<property name="processorBufferPoolType">0</property>
<!--默认是65535 64K 用于sql解析时最大文本长度 -->
<!--<property name="maxStringLiteralLength">65535</property>-->
<!--<property name="sequnceHandlerType">0</property>-->
<!--<property name="backSocketNoDelay">1</property>-->
<!--<property name="frontSocketNoDelay">1</property>-->
<!--<property name="processorExecutor">16</property>-->
<!--
<property name="serverPort">8066</property> <property name="managerPort">9066</property>
<property name="idleTimeout">300000</property> <property name="bindIp">0.0.0.0</property>
<property name="frontWriteQueueSize">4096</property> <property name="processors">32</property> -->
<!--分布式事务开关，0为不过滤分布式事务，1为过滤分布式事务（如果分布式事务内只涉及全局表，则不过滤），2为不过滤分布式事务,但是记录分布式事务日志-->
<property name="handleDistributedTransactions">0</property>
<!--
off heap for merge/order/group/limit 1开启 0关闭
-->
<property name="useOffHeapForMerge">1</property>
<!--
单位为m
-->
<property name="memoryPageSize">64k</property>
<!--
单位为k
-->
<property name="spillsFileBufferSize">1k</property>
<property name="useStreamOutput">0</property>
<!--
单位为m
-->
<property name="systemReserveMemorySize">384m</property>
<!--是否采用zookeeper协调切换 -->
<property name="useZKSwitch">false</property>
<!-- XA Recovery Log日志路径 -->
<!--<property name="XARecoveryLogBaseDir">./</property>-->
<!-- XA Recovery Log日志名称 -->
<!--<property name="XARecoveryLogBaseName">tmlog</property>-->
<!--如果为 true的话 严格遵守隔离级别,不会在仅仅只有select语句的时候在事务中切换连接-->
<property name="strictTxIsolation">false</property>
<property name="useZKSwitch">true</property>
</system>
<!-- 全局SQL防火墙设置 -->
<!--白名单可以使用通配符%或着*-->
<!--例如<host host="127.0.0.*" user="root"/>-->
<!--例如<host host="127.0.*" user="root"/>-->
<!--例如<host host="127.*" user="root"/>-->
<!--例如<host host="1*7.*" user="root"/>-->
<!--这些配置情况下对于127.0.0.1都能以root账户登录-->
<!-- 此处是开放了本机和外网的请求链接，因为我的mycat安装在虚拟机上，所以本机是通过ip链接
192.*是为了让navicat可以连接使用(配置完整的虚拟机IP也可以)
127.*是为了让虚拟机本身可以通过命令连接-->
<firewall>
<whitehost>
<host host="192.*" user="root"/>
<host host="127.*" user="root"/>
</whitehost>
<blacklist check="false">
</blacklist>
</firewall>
<!-- 此处定义了一个root用户，可以管理的逻辑库为mydatabase，对应schema.xml中的<schema name="mydatabase" > -->
<user name="root" defaultAccount="true">
<property name="password">123456</property>
<property name="schemas">mydatabase</property>
<!-- 表级 DML 权限设置 -->
<!--
<privileges check="false">
<schema name="TESTDB" dml="0110" >
<table name="tb01" dml="0000"></table>
<table name="tb02" dml="1111"></table>
</schema>
</privileges>
-->
</user>
<!--<user name="user">
<property name="password">123456</property>
<property name="schemas">mycat1,test3</property>
<property name="readOnly">true</property>
</user>-->
</mycat:server>
1.1.5.4. rule.xml配置
<?xml version="1.0" encoding="UTF-8"?>
<!-- - - Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License. - You
may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0
- - Unless required by applicable law or agreed to in writing, software -
distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the
License for the specific language governing permissions and - limitations
under the License. -->
<!DOCTYPE mycat:rule SYSTEM "rule.dtd">
<mycat:rule xmlns:mycat="http://io.mycat/">
<!--schema.xml中配置的rule="student_id" 所对应的规则，规则对应主键，
列名为id需要与数据库的列名对应algorithm对应下方function-->
<tableRule name="student_id">
<rule>
<columns>id</columns>
<algorithm>student_text</algorithm>
</rule>
</tableRule>
<tableRule name="rule1">
<rule>
<columns>id</columns>
<algorithm>func1</algorithm>
</rule>
</tableRule>
<tableRule name="rule2">
<rule>
<columns>user_id</columns>
<algorithm>func1</algorithm>
</rule>
</tableRule>
<tableRule name="sharding-by-intfile">
<rule>
<columns>sharding_id</columns>
<algorithm>hash-int</algorithm>
</rule>
</tableRule>
<tableRule name="auto-sharding-long">
<rule>
<columns>id</columns>
<algorithm>rang-long</algorithm>
</rule>
</tableRule>
<tableRule name="mod-long">
<rule>
<columns>id</columns>
<algorithm>mod-long</algorithm>
</rule>
</tableRule>
<tableRule name="sharding-by-murmur">
<rule>
<columns>id</columns>
<algorithm>murmur</algorithm>
</rule>
</tableRule>
<tableRule name="crc32slot">
<rule>
<columns>id</columns>
<algorithm>crc32slot</algorithm>
</rule>
</tableRule>
<tableRule name="sharding-by-month">
<rule>
<columns>create_time</columns>
<algorithm>partbymonth</algorithm>
</rule>
</tableRule>
<tableRule name="latest-month-calldate">
<rule>
<columns>calldate</columns>
<algorithm>latestMonth</algorithm>
</rule>
</tableRule>
<tableRule name="auto-sharding-rang-mod">
<rule>
<columns>id</columns>
<algorithm>rang-mod</algorithm>
</rule>
</tableRule>
<tableRule name="jch">
<rule>
<columns>id</columns>
<algorithm>jump-consistent-hash</algorithm>
</rule>
</tableRule>
<!--在conf中需要添加student_text.txt规则文件-->
<function name="student_text"
class="io.mycat.route.function.AutoPartitionByLong">
<property name="mapFile">student_text.txt</property>
</function>
<function name="murmur"
class="io.mycat.route.function.PartitionByMurmurHash">
<property name="seed">0</property><!-- 默认是0 -->
<property name="count">2</property><!-- 要分片的数据库节点数量，必须指定，否则没法分片 -->
<property name="virtualBucketTimes">160</property><!-- 一个实际的数据库节点被映射为这么多虚拟节点，默认是160倍，也就是虚拟节点数是物理节点数的160倍 -->
<!-- <property name="weightMapFile">weightMapFile</property> 节点的权重，没有指定权重的节点默认是1。以properties文件的格式填写，以从0开始到count-1的整数值也就是节点索引为key，以节点权重值为值。所有权重值必须是正整数，否则以1代替 -->
<!-- <property name="bucketMapPath">/etc/mycat/bucketMapPath</property>
用于测试时观察各物理节点与虚拟节点的分布情况，如果指定了这个属性，会把虚拟节点的murmur hash值与物理节点的映射按行输出到这个文件，没有默认值，如果不指定，就不会输出任何东西 -->
</function>
<function name="crc32slot"
class="io.mycat.route.function.PartitionByCRC32PreSlot">
</function>
<function name="hash-int"
class="io.mycat.route.function.PartitionByFileMap">
<property name="mapFile">partition-hash-int.txt</property>
</function>
<function name="rang-long"
class="io.mycat.route.function.AutoPartitionByLong">
<property name="mapFile">autopartition-long.txt</property>
</function>
<function name="mod-long" class="io.mycat.route.function.PartitionByMod">
<!-- how many data nodes -->
<property name="count">3</property>
</function>
<function name="func1" class="io.mycat.route.function.PartitionByLong">
<property name="partitionCount">8</property>
<property name="partitionLength">128</property>
</function>
<function name="latestMonth"
class="io.mycat.route.function.LatestMonthPartion">
<property name="splitOneDay">24</property>
</function>
<function name="partbymonth"
class="io.mycat.route.function.PartitionByMonth">
<property name="dateFormat">yyyy-MM-dd</property>
<property name="sBeginDate">2015-01-01</property>
</function>
<function name="rang-mod" class="io.mycat.route.function.PartitionByRangeMod">
<property name="mapFile">partition-range-mod.txt</property>
</function>
<function name="jump-consistent-hash" class="io.mycat.route.function.PartitionByJumpConsistentHash">
<property name="totalBuckets">3</property>
</function>
</mycat:rule>
1.2. 读写分离实现
1.3. 常见报错
1.3.1. 启动常见报错：
INFO | jvm 1 | 2021/02/09 15:34:54 | WrapperSimpleApp Usage:
INFO | jvm 1 | 2021/02/09 15:34:54 | java org.tanukisoftware.wrapper.WrapperSimpleApp {app_class} [app_arguments]
INFO | jvm 1 | 2021/02/09 15:34:54 |
INFO | jvm 1 | 2021/02/09 15:34:54 | Where:
INFO | jvm 1 | 2021/02/09 15:34:54 | app_class: The fully qualified class name of the application to run.
INFO | jvm 1 | 2021/02/09 15:34:54 | app_arguments: The arguments that would normally be passed to the
INFO | jvm 1 | 2021/02/09 15:34:54 | application.
以上报错安装jdk8的版本即可处理
1.3.2. ERROR 1152 (HY000): java.lang.IllegalArgumentException: Invalid DataSource:0
问题分析：无效的数据源，我这的情况是由于mycat的逻辑库无法获得真实库的数据导致的错误。
问题解决：我这新安装的mysql(Percona)，在设置本地登录root/root用户权限后：mysqladmin -uroot password ‘root’,没有给mysql其它通过ip或者本地127.0.0.1登录方式添加密码，因此如果通过mycat登录时有密码的话，就匹配不上，所以mycat无法获取源数据库的数据导致错误，只要通过SQLyog登录上mysql的user表格（mysql->user)，直接localhost上密码复制到其它ip、127.0.0.1等行password上就好了。顺手直接把其它的登录方式密码也直接复制粘贴并保存，重新启动数据库：service mysql restart 。就可以通过mycat重新连接上了。或者通过命令行登录上mysql去修改对应表格。
1.3.3. no route to host
防火墙问题