前言
垂直分庫是指按照業務將表進行分類，分佈到不同的數據庫上面，每個庫可以放在不同的服務器
上，它的核心理念是專庫專用。接下來看一下如何使用Sharding-JDBC實現垂直分庫。
創建數據庫
創建數據庫user_db
CREATE DATABASE `user_db` CHARACTER SET 'utf8' COLLATE 'utf8_general_ci';
在user_db中創建t_user表
DROP TABLE IF EXISTS `t_user`;
CREATE TABLE `t_user` (
`user_id` bigint(20) NOT NULL COMMENT '用戶id',
`fullname` varchar(255) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '用戶姓名',
`user_type` char(1) DEFAULT NULL COMMENT '用戶類型',
PRIMARY KEY (`user_id`) USING BTREE
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;
分片規則配置
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分片規則配置
#數據源
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表的數據分佈情況，配置數據節點 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表的主鍵生成策略爲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，分片策略包括分片鍵和分片算法
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}
# 分庫策略，以user_id爲分片鍵，分片策略爲user_id % 2 + 1，user_id爲偶數操作m1數據源，否則操作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
# 打開sql輸出日誌
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
數據操作
新增UserDao:
@Mapper
@Component
public interface UserDao {
/**
* 新增用戶
* @param userId 用戶id
* @param fullname 用戶姓名
* @return
*/
@Insert("insert into t_user(user_id, fullname) value(#{userId},#{fullname})")
int insertUser(@Param("userId")Long userId, @Param("fullname")String fullname);
/**
* 根據id列表查詢多個用戶
* @param userIds 用戶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);
}
測試
@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);
}
執行testInsertUser:
通過日誌可以看出t_user表的數據被落在了m0數據源，達到目標。
執行testSelectUserbyIds:
通過日誌可以看出t_user表的查詢操作被落在了m0數據源，達到目標。
公共表
公共表屬於系統中數據量較小，變動少，而且屬於高頻聯合查詢的依賴表。參數表、數據字典表等屬於此類型。可以將這類表在每個數據庫都保存一份，所有更新操作都同時發送到所有分庫執行。接下來看一下如何使用
Sharding-JDBC實現公共表。
創建數據庫
分別在user_db、order_db_1、order_db_2中創建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 '字典類型',
`code` varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '字典編碼',
`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規則中修改
# 指定t_dict爲公共表
spring.shardingsphere.sharding.broadcast‐tables=t_dict
數據操作
新增DictDao：
@Mapper
@Component
public interface DictDao {
/**
* 新增字典
*
* @param type 字典類型
* @param code 字典編碼
* @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);
/**
* 刪除字典
*
* @param dictId 字典id
* @return
*/
@Delete("delete from t_dict where dict_id = #{dictId}")
int deleteDict(@Param("dictId") Long dictId);
}
字典操作測試
新增單元測試方法：
@Test
public void testInsertDict(){
dictDao.insertDict(1L,"user_type","0","管理員");
dictDao.insertDict(2L,"user_type","1","操作員");
}
@Test
public void testDeleteDict(){
dictDao.deleteDict(1L);
dictDao.deleteDict(2L);
}
執行testInsertDict：
通過日誌可以看出，對t_dict的表的操作被廣播至所有數據源。
測試刪除字典，觀察是否把所有數據源中該 公共表的記錄刪除。