前言
垂直分库是指按照业务将表进行分类，分布到不同的数据库上面，每个库可以放在不同的服务器
上，它的核心理念是专库专用。接下来看一下如何使用Sharding-JDBC实现垂直分库。
创建数据库
创建数据库user_db
CREATE DATABASE `user_db` CHARACTER SET 'utf8' COLLATE 'utf8_general_ci';
在user_db中创建t_user表
DROP TABLE IF EXISTS `t_user`;
CREATE TABLE `t_user` (
`user_id` bigint(20) NOT NULL COMMENT '用户id',
`fullname` varchar(255) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '用户姓名',
`user_type` char(1) DEFAULT NULL COMMENT '用户类型',
PRIMARY KEY (`user_id`) USING BTREE
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;
分片规则配置
server.port=56081
spring.application.name = fast-common-sharding-horizontal-subdivision
server.servlet.context-path = /fast-common-sharding-horizontal-subdivision
spring.http.encoding.enabled = true
spring.http.encoding.charset = UTF-8
spring.http.encoding.force = true
spring.main.allow-bean-definition-overriding = true
mybatis.configuration.map-underscore-to-camel-case = true
#sharding-jdbc分片规则配置
#数据源
spring.shardingsphere.datasource.names = m0,m1,m2
spring.shardingsphere.datasource.m0.type = com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.m0.driver-class-name = com.mysql.cj.jdbc.Driver
spring.shardingsphere.datasource.m0.url = jdbc:mysql://192.168.56.121:33065/user_db?useUnicode=true
spring.shardingsphere.datasource.m0.username = root
spring.shardingsphere.datasource.m0.password = root
spring.shardingsphere.datasource.m1.type = com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.m1.driver-class-name = com.mysql.cj.jdbc.Driver
spring.shardingsphere.datasource.m1.url = jdbc:mysql://192.168.56.121:33065/order_db_1?useUnicode=true
spring.shardingsphere.datasource.m1.username = root
spring.shardingsphere.datasource.m1.password = root
spring.shardingsphere.datasource.m2.type = com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.m2.driver‐class‐name = com.mysql.jdbc.Driver
spring.shardingsphere.datasource.m2.url = jdbc:mysql://192.168.56.121:33065/order_db_2?useUnicode=true
spring.shardingsphere.datasource.m2.username = root
spring.shardingsphere.datasource.m2.password = root
# 指定t_order表的数据分布情况，配置数据节点 m1.t_order_1,m1.t_order_2,m2.t_order_1,m2.t_order_2
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes = m$->{1..2}.t_order_$->{1..2}
#spring.shardingsphere.sharding.tables.t_user.actual-data-nodes = m$->{0}.t_user
spring.shardingsphere.sharding.tables.t_user.actual-data-nodes = m$->{0}.t_user
# 指定t_order表的主键生成策略为SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
# 指定t_order表的分片策略，分片策略包括分片键和分片算法
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column = order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression = t_order_$->{order_id % 2 + 1}
# 分库策略，以user_id为分片键，分片策略为user_id % 2 + 1，user_id为偶数操作m1数据源，否则操作m2。
spring.shardingsphere.sharding.tables.t_user.table-strategy.inline.sharding-column= user_id
spring.shardingsphere.sharding.tables.t_user.table-strategy.inline.algorithm-expression = t_user
# 打开sql输出日志
spring.shardingsphere.props.sql.show = true
swagger.enable = true
logging.level.root = info
logging.level.org.springframework.web = info
fast.cloud.nacos.horizontal.subdivision = debug
logging.level.druid.sql = debug
数据操作
新增UserDao:
@Mapper
@Component
public interface UserDao {
/**
* 新增用户
* @param userId 用户id
* @param fullname 用户姓名
* @return
*/
@Insert("insert into t_user(user_id, fullname) value(#{userId},#{fullname})")
int insertUser(@Param("userId")Long userId, @Param("fullname")String fullname);
/**
* 根据id列表查询多个用户
* @param userIds 用户id列表
* @return
*/
@Select({"<script>",
" select",
" * ",
" from t_user t ",
" where t.user_id in",
"<foreach collection='userIds' item='id' open='(' separator=',' close=')'>",
"#{id}",
"</foreach>",
"</script>"
})
List<Map> selectUserbyIds(@Param("userIds") List<Long> userIds);
}
测试
@Test
public void testInsertUser(){
for (int i = 0 ; i<10; i++){
Long id = i + 1L;
userDao.insertUser(id,"姓名"+ id );
}
}
@Test
public void testSelectUserbyIds(){
List<Long> userIds = new ArrayList<>();
userIds.add(1L);
userIds.add(2L);
List<Map> users = userDao.selectUserbyIds(userIds);
System.out.println(users);
}
执行testInsertUser:
通过日志可以看出t_user表的数据被落在了m0数据源，达到目标。
执行testSelectUserbyIds:
通过日志可以看出t_user表的查询操作被落在了m0数据源，达到目标。
公共表
公共表属于系统中数据量较小，变动少，而且属于高频联合查询的依赖表。参数表、数据字典表等属于此类型。可以将这类表在每个数据库都保存一份，所有更新操作都同时发送到所有分库执行。接下来看一下如何使用
Sharding-JDBC实现公共表。
创建数据库
分别在user_db、order_db_1、order_db_2中创建t_dict表：
CREATE TABLE `t_dict` (
`dict_id` bigint(20) NOT NULL COMMENT '字典id',
`type` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '字典类型',
`code` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '字典编码',
`value` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '字典值',
PRIMARY KEY (`dict_id`) USING BTREE
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;
在Sharding-JDBC规则中修改
# 指定t_dict为公共表
spring.shardingsphere.sharding.broadcast‐tables=t_dict
数据操作
新增DictDao：
@Mapper
@Component
public interface DictDao {
/**
* 新增字典
*
* @param type 字典类型
* @param code 字典编码
* @param value 字典值
* @return
*/
@Insert("insert into t_dict(dict_id,type,code,value) value(#{dictId},#{type},#{code},# {value})")
int insertDict(@Param("dictId") Long dictId, @Param("type") String type, @Param("code") String
code, @Param("value") String value);
/**
* 删除字典
*
* @param dictId 字典id
* @return
*/
@Delete("delete from t_dict where dict_id = #{dictId}")
int deleteDict(@Param("dictId") Long dictId);
}
字典操作测试
新增单元测试方法：
@Test
public void testInsertDict(){
dictDao.insertDict(1L,"user_type","0","管理员");
dictDao.insertDict(2L,"user_type","1","操作员");
}
@Test
public void testDeleteDict(){
dictDao.deleteDict(1L);
dictDao.deleteDict(2L);
}
执行testInsertDict：
通过日志可以看出，对t_dict的表的操作被广播至所有数据源。
测试删除字典，观察是否把所有数据源中该 公共表的记录删除。