SpringBoot结合ShardingSphere实现分库分表、读写分离

这次在上一篇的基础上，这次用到4个库，将库db0、db1各增加一个从库dbS0dbS1

四个库再执行脚本：

-- ----------------------------
-- Table structure for `user0`
-- ----------------------------
DROP TABLE IF EXISTS `user0`;
CREATE TABLE `user0` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`name` varchar(10) DEFAULT NULL,
`age` int(11) DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=105 DEFAULT CHARSET=utf8mb4;

-- ----------------------------
-- Records of user0
-- ----------------------------
INSERT INTO `user0` VALUES ('1', 'wangxin02', '99');

-- ----------------------------
-- Table structure for `user1`
-- ----------------------------
DROP TABLE IF EXISTS `user1`;
CREATE TABLE `user1` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`name` varchar(10) DEFAULT NULL,
`age` int(11) DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=1000 DEFAULT CHARSET=utf8mb4;

-- ----------------------------
-- Records of user1
-- ----------------------------
INSERT INTO `user1` VALUES ('1', 'wangxin', '99');

只分表不分库

首先先只分表不分库，使用master来执行写操作

核心配置

#数据分表规则--仅分表不分库
#注意：tables.user中的user是逻辑表
#指定所需分的表，分user1、user2
spring.shardingsphere.sharding.tables.user.actual-data-nodes=master.user$->{0..1}

#行表达式分片策略
spring.shardingsphere.sharding.tables.user.table-strategy.inline.sharding-column=id
#分表两个，所以规则为主键除以2取模
spring.shardingsphere.sharding.tables.user.table-strategy.inline.algorithm-expression=user$->{id % 2}

##用于单分片键的标准分片场景
##指定自增主键
#spring.shardingsphere.sharding.tables.user.key-generator.column= id
##自增列值生成器类型，缺省表示使用默认自增列值生成器。可使用用户自定义的列值生成器或选择内置类型：SNOWFLAKE/UUID
#spring.shardingsphere.sharding.tables.user.key-generator.type= SNOWFLAKE

map新增

  <insert id="insertSharding" parameterType="com.xin.shardingspherejdbcdemo.entity.User">
    insert into user (id,name, age)
    values ( #{id,jdbcType=INTEGER},#{name,jdbcType=VARCHAR},
      #{age,jdbcType=INTEGER})
  </insert>

控制器新增

@GetMapping("/saveUserShardingOnlyTable")
    public void saveUserShardingOnlyTable() {
        List<User> userList = Lists.newArrayList();
        userList.add(new User(100,"用户1",  31));
        userList.add(new User(101,"用户2",  30));
        userList.add(new User(102,"用户3",  28));
        userList.add(new User(103,"用户4",  64));
        userList.add(new User(104,"用户5",  62));
        userList.add(new User(105,"用户6",  16));
        for (User u : userList){
            userService.saveSharding(u);
        }
    }

执行saveUserShardingOnlyTable后，我们发现db0中的两个表分别加入了3条记录

user0加入：

100 用户1 31
102 用户3 28
104 用户5 62

user1加入：
101 用户2 30
103 用户4 64
105 用户6 16

日志：

2020-05-22 14:33:33.807 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Actual SQL: master ::: insert into user0 (id,name, age)
values (?, ?, ?) ::: [100, 用户1, 31]
2020-05-22 14:33:33.944 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Rule Type: sharding
2020-05-22 14:33:33.945 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Logic SQL: insert into user (id,name, age)
values ( ?,?,
?)
2020-05-22 14:33:33.945 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : SQLStatement: InsertSQLStatementContext(super=CommonSQLStatementContext(sqlStatement=org.apache.shardingsphere.sql.parser.sql.statement.dml.InsertStatement@8a9d30d, tablesContext=TablesContext(tables=[Table(name=user, alias=Optional.absent())], schema=Optional.absent())), columnNames=[id, name, age], insertValueContexts=[InsertValueContext(parametersCount=3, valueExpressions=[ParameterMarkerExpressionSegment(startIndex=45, stopIndex=45, parameterMarkerIndex=0), ParameterMarkerExpressionSegment(startIndex=47, stopIndex=47, parameterMarkerIndex=1), ParameterMarkerExpressionSegment(startIndex=56, stopIndex=56, parameterMarkerIndex=2)], parameters=[101, 用户2, 30])])
2020-05-22 14:33:33.945 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Actual SQL: master ::: insert into user1 (id,name, age)
values (?, ?, ?) ::: [101, 用户2, 30]
2020-05-22 14:33:33.985 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Rule Type: sharding
2020-05-22 14:33:33.985 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Logic SQL: insert into user (id,name, age)
values ( ?,?,
?)
2020-05-22 14:33:33.985 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : SQLStatement: InsertSQLStatementContext(super=CommonSQLStatementContext(sqlStatement=org.apache.shardingsphere.sql.parser.sql.statement.dml.InsertStatement@8a9d30d, tablesContext=TablesContext(tables=[Table(name=user, alias=Optional.absent())], schema=Optional.absent())), columnNames=[id, name, age], insertValueContexts=[InsertValueContext(parametersCount=3, valueExpressions=[ParameterMarkerExpressionSegment(startIndex=45, stopIndex=45, parameterMarkerIndex=0), ParameterMarkerExpressionSegment(startIndex=47, stopIndex=47, parameterMarkerIndex=1), ParameterMarkerExpressionSegment(startIndex=56, stopIndex=56, parameterMarkerIndex=2)], parameters=[102, 用户3, 28])])
2020-05-22 14:33:33.985 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Actual SQL: master ::: insert into user0 (id,name, age)
values (?, ?, ?) ::: [102, 用户3, 28]
2020-05-22 14:33:34.043 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Rule Type: sharding
2020-05-22 14:33:34.043 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Logic SQL: insert into user (id,name, age)
values ( ?,?,
?)
2020-05-22 14:33:34.043 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : SQLStatement: InsertSQLStatementContext(super=CommonSQLStatementContext(sqlStatement=org.apache.shardingsphere.sql.parser.sql.statement.dml.InsertStatement@8a9d30d, tablesContext=TablesContext(tables=[Table(name=user, alias=Optional.absent())], schema=Optional.absent())), columnNames=[id, name, age], insertValueContexts=[InsertValueContext(parametersCount=3, valueExpressions=[ParameterMarkerExpressionSegment(startIndex=45, stopIndex=45, parameterMarkerIndex=0), ParameterMarkerExpressionSegment(startIndex=47, stopIndex=47, parameterMarkerIndex=1), ParameterMarkerExpressionSegment(startIndex=56, stopIndex=56, parameterMarkerIndex=2)], parameters=[103, 用户4, 64])])
2020-05-22 14:33:34.043 INFO 9952 --- [nio-8086-exec-1] ShardingSphere-SQL : Actual SQL: master ::: insert into user1 (id,name, age)
values (?, ?, ?) ::: [103, 用户4, 64]

分库 + 分表同时进行

核心配置

server.port=8086

#指定mybatis信息
mybatis.config-location=classpath:mybatis-config.xml

# 数据源 主库、从库 -- 读写分离
#spring.shardingsphere.datasource.names=master,slave0
# 数据源 主库 --仅分表不分库
#spring.shardingsphere.datasource.names=master
# 数据源 主库db0、主库db1 -- 分库分表
spring.shardingsphere.datasource.names=master0,master1


# 数据源 主库
spring.shardingsphere.datasource.master0.type=com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.master0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.master0.url=jdbc:mysql://localhost:3307/db0?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai
spring.shardingsphere.datasource.master0.username=root
spring.shardingsphere.datasource.master0.password=mysql

# 数据源 主库
spring.shardingsphere.datasource.master1.type=com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.master1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.master1.url=jdbc:mysql://localhost:3307/db1?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai
spring.shardingsphere.datasource.master1.username=root
spring.shardingsphere.datasource.master1.password=mysql

# 数据源 从库
spring.shardingsphere.datasource.slave0.type=com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.slave0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.slave0.url=jdbc:mysql://localhost:3307/db1?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai
spring.shardingsphere.datasource.slave0.username=root
spring.shardingsphere.datasource.slave0.password=mysql

## 读写分离
##spring.shardingsphere.masterslave.load-balance-algorithm-type=round_robin
#spring.shardingsphere.masterslave.name=ms
#spring.shardingsphere.masterslave.master-data-source-name=master
#spring.shardingsphere.masterslave.slave-data-source-names=slave0

## 分库
#根据ID分库
#spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=id
#spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=master$->{id % 2}

#根据年龄分库
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=age
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=master$->{age > 30?0:1}

#数据分表规则--仅分表不分库
#注意：tables.user中的user是逻辑表
#仅分表时使用，单一写表，指定所需分的表，分user1、user2
#spring.shardingsphere.sharding.tables.user.actual-data-nodes=master.user$->{0..1}
#同时分库分表时使用
spring.shardingsphere.sharding.tables.user.actual-data-nodes=master$->{0..1}.user$->{0..1}

#行表达式分片策略
spring.shardingsphere.sharding.tables.user.table-strategy.inline.sharding-column=id
#分表两个，所以规则为主键除以2取模
spring.shardingsphere.sharding.tables.user.table-strategy.inline.algorithm-expression=user$->{id % 2}

##用于单分片键的标准分片场景
##指定自增主键
#spring.shardingsphere.sharding.tables.user.key-generator.column= id
##自增列值生成器类型，缺省表示使用默认自增列值生成器。可使用用户自定义的列值生成器或选择内置类型：SNOWFLAKE/UUID
#spring.shardingsphere.sharding.tables.user.key-generator.type= SNOWFLAKE

#打印sql
spring.shardingsphere.props.sql.show=true

控制器

    @GetMapping("/saveUserShardingDBwithTable")
    public void saveUserShardingDBwithTable() {
        List<User> userList = Lists.newArrayList();
        userList.add(new User(10000,"用户1",  31));
        userList.add(new User(10001,"用户2",  30));
        userList.add(new User(10002,"用户3",  28));
        userList.add(new User(10003,"用户4",  64));
        userList.add(new User(10004,"用户5",  62));
        userList.add(new User(10005,"用户6",  16));
        for (User u : userList){
            userService.saveSharding(u);
        }
    }

分库分表+读写分离

只需要在上面的基础上添加读写分离的配置即可

#指定master0为主库，slave0为它的从库
spring.shardingsphere.sharding.master-slave-rules.master0.master-data-source-name=master0
spring.shardingsphere.sharding.master-slave-rules.master0.slave-data-source-names=slave0
#指定master1为主库，slave1为它的从库
spring.shardingsphere.sharding.master-slave-rules.master1.master-data-source-name=master1
spring.shardingsphere.sharding.master-slave-rules.master1.slave-data-source-names=slave1

完整配置说明

数据分片
spring.shardingsphere.datasource.names= #数据源名称，多数据源以逗号分隔

spring.shardingsphere.datasource.<data-source-name>.type= #数据库连接池类名称
spring.shardingsphere.datasource.<data-source-name>.driver-class-name= #数据库驱动类名
spring.shardingsphere.datasource.<data-source-name>.url= #数据库url连接
spring.shardingsphere.datasource.<data-source-name>.username= #数据库用户名
spring.shardingsphere.datasource.<data-source-name>.password= #数据库密码
spring.shardingsphere.datasource.<data-source-name>.xxx= #数据库连接池的其它属性

spring.shardingsphere.sharding.tables.<logic-table-name>.actual-data-nodes= #由数据源名 + 表名组成，以小数点分隔。多个表以逗号分隔，支持inline表达式。缺省表示使用已知数据源与逻辑表名称生成数据节点，用于广播表（即每个库中都需要一个同样的表用于关联查询，多为字典表）或只分库不分表且所有库的表结构完全一致的情况

#分库策略，缺省表示使用默认分库策略，以下的分片策略只能选其一

#用于单分片键的标准分片场景
spring.shardingsphere.sharding.tables.<logic-table-name>.database-strategy.standard.sharding-column= #分片列名称
spring.shardingsphere.sharding.tables.<logic-table-name>.database-strategy.standard.precise-algorithm-class-name= #精确分片算法类名称，用于=和IN。该类需实现PreciseShardingAlgorithm接口并提供无参数的构造器
spring.shardingsphere.sharding.tables.<logic-table-name>.database-strategy.standard.range-algorithm-class-name= #范围分片算法类名称，用于BETWEEN，可选。该类需实现RangeShardingAlgorithm接口并提供无参数的构造器

#用于多分片键的复合分片场景
spring.shardingsphere.sharding.tables.<logic-table-name>.database-strategy.complex.sharding-columns= #分片列名称，多个列以逗号分隔
spring.shardingsphere.sharding.tables.<logic-table-name>.database-strategy.complex.algorithm-class-name= #复合分片算法类名称。该类需实现ComplexKeysShardingAlgorithm接口并提供无参数的构造器

#行表达式分片策略
spring.shardingsphere.sharding.tables.<logic-table-name>.database-strategy.inline.sharding-column= #分片列名称
spring.shardingsphere.sharding.tables.<logic-table-name>.database-strategy.inline.algorithm-expression= #分片算法行表达式，需符合groovy语法

#Hint分片策略
spring.shardingsphere.sharding.tables.<logic-table-name>.database-strategy.hint.algorithm-class-name= #Hint分片算法类名称。该类需实现HintShardingAlgorithm接口并提供无参数的构造器

#分表策略，同分库策略
spring.shardingsphere.sharding.tables.<logic-table-name>.table-strategy.xxx= #省略

spring.shardingsphere.sharding.tables.<logic-table-name>.key-generator.column= #自增列名称，缺省表示不使用自增主键生成器
spring.shardingsphere.sharding.tables.<logic-table-name>.key-generator.type= #自增列值生成器类型，缺省表示使用默认自增列值生成器。可使用用户自定义的列值生成器或选择内置类型：SNOWFLAKE/UUID
spring.shardingsphere.sharding.tables.<logic-table-name>.key-generator.props.<property-name>= #属性配置, 注意：使用SNOWFLAKE算法，需要配置worker.id与max.tolerate.time.difference.milliseconds属性。若使用此算法生成值作分片值，建议配置max.vibration.offset属性

spring.shardingsphere.sharding.binding-tables[0]= #绑定表规则列表
spring.shardingsphere.sharding.binding-tables[1]= #绑定表规则列表
spring.shardingsphere.sharding.binding-tables[x]= #绑定表规则列表

spring.shardingsphere.sharding.broadcast-tables[0]= #广播表规则列表
spring.shardingsphere.sharding.broadcast-tables[1]= #广播表规则列表
spring.shardingsphere.sharding.broadcast-tables[x]= #广播表规则列表

spring.shardingsphere.sharding.default-data-source-name= #未配置分片规则的表将通过默认数据源定位
spring.shardingsphere.sharding.default-database-strategy.xxx= #默认数据库分片策略，同分库策略
spring.shardingsphere.sharding.default-table-strategy.xxx= #默认表分片策略，同分表策略
spring.shardingsphere.sharding.default-key-generator.type= #默认自增列值生成器类型，缺省将使用org.apache.shardingsphere.core.keygen.generator.impl.SnowflakeKeyGenerator。可使用用户自定义的列值生成器或选择内置类型：SNOWFLAKE/UUID
spring.shardingsphere.sharding.default-key-generator.props.<property-name>= #自增列值生成器属性配置, 比如SNOWFLAKE算法的worker.id与max.tolerate.time.difference.milliseconds

spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.master-data-source-name= #详见读写分离部分
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.slave-data-source-names[0]= #详见读写分离部分
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.slave-data-source-names[1]= #详见读写分离部分
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.slave-data-source-names[x]= #详见读写分离部分
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.load-balance-algorithm-class-name= #详见读写分离部分
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.load-balance-algorithm-type= #详见读写分离部分

spring.shardingsphere.props.sql.show= #是否开启SQL显示，默认值: false
spring.shardingsphere.props.executor.size= #工作线程数量，默认值: CPU核数
读写分离
#省略数据源配置，与数据分片一致

spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.master-data-source-name= #主库数据源名称
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.slave-data-source-names[0]= #从库数据源名称列表
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.slave-data-source-names[1]= #从库数据源名称列表
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.slave-data-source-names[x]= #从库数据源名称列表
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.load-balance-algorithm-class-name= #从库负载均衡算法类名称。该类需实现MasterSlaveLoadBalanceAlgorithm接口且提供无参数构造器
spring.shardingsphere.sharding.master-slave-rules.<master-slave-data-source-name>.load-balance-algorithm-type= #从库负载均衡算法类型，可选值：ROUND_ROBIN，RANDOM。若`load-balance-algorithm-class-name`存在则忽略该配置

spring.shardingsphere.props.sql.show= #是否开启SQL显示，默认值: false
spring.shardingsphere.props.executor.size= #工作线程数量，默认值: CPU核数
spring.shardingsphere.props.check.table.metadata.enabled= #是否在启动时检查分表元数据一致性，默认值: false
数据脱敏
#省略数据源配置，与数据分片一致

spring.shardingsphere.encrypt.encryptors.<encryptor-name>.type= #加解密器类型，可自定义或选择内置类型：MD5/AES 
spring.shardingsphere.encrypt.encryptors.<encryptor-name>.props.<property-name>= #属性配置, 注意：使用AES加密器，需要配置AES加密器的KEY属性：aes.key.value
spring.shardingsphere.encrypt.tables.<table-name>.columns.<logic-column-name>.plainColumn= #存储明文的字段
spring.shardingsphere.encrypt.tables.<table-name>.columns.<logic-column-name>.cipherColumn= #存储密文的字段
spring.shardingsphere.encrypt.tables.<table-name>.columns.<logic-column-name>.assistedQueryColumn= #辅助查询字段，针对ShardingQueryAssistedEncryptor类型的加解密器进行辅助查询
spring.shardingsphere.encrypt.tables.<table-name>.columns.<logic-column-name>.encryptor= #加密器名字

 后台原理

ShardingSphere的核心由SQL解析 => 执行器优化 => SQL路由 => SQL改写 => SQL执行 => 结果归并的流程组成。

SQL解析

分为词法解析和语法解析。 先通过词法解析器将SQL拆分为一个个不可再分的单词。再使用语法解析器对SQL进行理解，并最终提炼出解析上下文。 解析上下文包括表、选择项、排序项、分组项、聚合函数、分页信息、查询条件以及可能需要修改的占位符的标记。

执行器优化

合并和优化分片条件，如OR等。

SQL路由

根据解析上下文匹配用户配置的分片策略，并生成路由路径。目前支持分片路由和广播路由。

SQL改写

将SQL改写为在真实数据库中可以正确执行的语句。SQL改写分为正确性改写和优化改写。

SQL执行

通过多线程执行器异步执行。

结果归并

将多个执行结果集归并以便于通过统一的JDBC接口输出。结果归并包括流式归并、内存归并和使用装饰者模式的追加归并这几种方式。

具体见：https://shardingsphere.apache.org/document/current/cn/features/sharding/principle/

全部源码

shardingsphere-jdbc-demo-all