0103水平分片-jdbc-shardingsphere-中间件

文章目录

1 准备服务器

随着系统业务的发展，t_order表数据快速增长，服务器压力增大，影响系统性能，我需要对server-order进行分库分表。

服务器规划：

在这里插入图片描述

服务器：容器名server-order0，端口号3310
服务器：容器名server-order1，端口号3311

1.1 创建server-order0容器

step1：创建挂载文件夹mkdir-p server-order0/conf/conf.dmkdir server-order0/data
Step2：创建容器docker run -it-p3310:3306 --name server-order0 --privileged=true -v /Users/gaogzhen/data/docker/mysql/mysql8/server-order0/conf/conf.d:/etc/mysql/conf.d -v /Users/gaogzhen/data/docker/mysql/mysql8/server-order0/data:/var/lib/mysql -eMYSQL_ROOT_PASSWORD=123456-d mysql- step3：登录MySQL服务器：#进入容器：dockerexec-it server-order0 envLANG=C.UTF-8 /bin/bash#进入容器内的mysql命令行mysql -uroot-p#修改默认密码插件ALTER USER'root'@'%' IDENTIFIED WITH mysql_native_password BY '123456';- step4：创建数据库：注意：水平分片的id需要在业务层实现，不能依赖数据库的主键自增``````CREATEDATABASE db_order;USE db_order;CREATETABLE t_order0 ( id BIGINT, order_no VARCHAR(30), user_id BIGINT, amount DECIMAL(10,2),PRIMARYKEY(id));CREATETABLE t_order1 ( id BIGINT, order_no VARCHAR(30), user_id BIGINT, amount DECIMAL(10,2),PRIMARYKEY(id));

1.2 创建server-order1容器

step1：创建挂载文件夹mkdir-p server-order1/conf/conf.dmkdir server-order1/data
Step2：创建容器docker run -it-p3311:3306 --name server-order1 --privileged=true -v /Users/gaogzhen/data/docker/mysql/mysql8/server-order1/conf/conf.d:/etc/mysql/conf.d -v /Users/gaogzhen/data/docker/mysql/mysql8/server-order1/data:/var/lib/mysql -eMYSQL_ROOT_PASSWORD=123456-d mysql- step3：登录MySQL服务器：#进入容器：dockerexec-it server-order0 envLANG=C.UTF-8 /bin/bash#进入容器内的mysql命令行mysql -uroot-p#修改默认密码插件ALTER USER'root'@'%' IDENTIFIED WITH mysql_native_password BY '123456';- step4：创建数据库：注意：水平分片的id需要在业务层实现，不能依赖数据库的主键自增``````CREATEDATABASE db_order;USE db_order;CREATETABLE t_order0 ( id BIGINT, order_no VARCHAR(30), user_id BIGINT, amount DECIMAL(10,2),PRIMARYKEY(id));CREATETABLE t_order1 ( id BIGINT, order_no VARCHAR(30), user_id BIGINT, amount DECIMAL(10,2),PRIMARYKEY(id));

2、基本水平分片

2.1、基本配置

#========================基本配置
# 应用名称
spring.application.name=sharging-jdbc-demo
# 开发环境设置
spring.profiles.active=dev
# 内存模式
spring.shardingsphere.mode.type=Memory
# 打印SQl
spring.shardingsphere.props.sql-show=true

2.2、数据源配置

#========================数据源配置
# 配置真实数据源
spring.shardingsphere.datasource.names=server-user,server-order0,server-order1

# 配置第 1 个数据源
spring.shardingsphere.datasource.server-user.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.server-user.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.server-user.jdbc-url=jdbc:mysql://192.168.100.201:3301/db_user
spring.shardingsphere.datasource.server-user.username=root
spring.shardingsphere.datasource.server-user.password=123456

# 配置第 2 个数据源
spring.shardingsphere.datasource.server-order0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.server-order0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.server-order0.jdbc-url=jdbc:mysql://192.168.100.201:3310/db_order
spring.shardingsphere.datasource.server-order0.username=root
spring.shardingsphere.datasource.server-order0.password=123456

# 配置第 3 个数据源
spring.shardingsphere.datasource.server-order1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.server-order1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.server-order1.jdbc-url=jdbc:mysql://192.168.100.201:3311/db_order
spring.shardingsphere.datasource.server-order1.username=root
spring.shardingsphere.datasource.server-order1.password=123456

2.3、标椎分片表配置

#========================标准分片表配置（数据节点配置）
# spring.shardingsphere.rules.sharding.tables.<table-name>.actual-data-nodes=值
# 值由数据源名 + 表名组成，以小数点分隔。多个表以逗号分隔，支持 inline 表达式。
# <table-name>：逻辑表名
spring.shardingsphere.rules.sharding.tables.t_user.actual-data-nodes=server-user.t_user
spring.shardingsphere.rules.sharding.tables.t_order.actual-data-nodes=server-order0.t_order0,server-order0.t_order1,server-order1.t_order0,server-order1.t_order1

修改Order实体类的主键策略：

//@TableId(type = IdType.AUTO)//依赖数据库的主键自增策略@TableId(type =IdType.ASSIGN_ID)//分布式id

测试：保留上面配置中的一个分片表节点分别进行测试，检查每个分片节点是否可用

/**
     * 水平分片：插入数据测试
     */@TestpublicvoidtestInsertOrder(){Order order =newOrder();
    order.setOrderNo("20230822001");
    order.setUserId(1L);
    order.setAmount(newBigDecimal(100));
    orderMapper.insert(order);}

2.4、行表达式

优化上一步的分片表配置

https://shardingsphere.apache.org/document/5.1.1/cn/features/sharding/concept/inline-expression/

#========================标准分片表配置（数据节点配置）
# spring.shardingsphere.rules.sharding.tables.<table-name>.actual-data-nodes=值
# 值由数据源名 + 表名组成，以小数点分隔。多个表以逗号分隔，支持 inline 表达式。
# <table-name>：逻辑表名
spring.shardingsphere.rules.sharding.tables.t_user.actual-data-nodes=server-user.t_user
spring.shardingsphere.rules.sharding.tables.t_order.actual-data-nodes=server-order$->{0..1}.t_order$->{0..1}

2.5、分片算法配置

水平分库：

分片规则：order表中

user_id

为偶数时，数据插入

server-order0服务器

，

user_id

为奇数时，数据插入

server-order1服务器

。这样分片的好处是，同一个用户的订单数据，一定会被插入到同一台服务器上，查询一个用户的订单时效率较高。

#------------------------分库策略
# 分片列名称
spring.shardingsphere.rules.sharding.tables.t_order.database-strategy.standard.sharding-column=user_id
# 分片算法名称
spring.shardingsphere.rules.sharding.tables.t_order.database-strategy.standard.sharding-algorithm-name=alg_inline_userid

#------------------------分片算法配置
# 行表达式分片算法
# 分片算法类型
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_inline_userid.type=INLINE
# 分片算法属性配置
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_inline_userid.props.algorithm-expression=server-order$->{user_id % 2}

# 取模分片算法
# 分片算法类型
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_mod.type=MOD
# 分片算法属性配置
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_mod.props.sharding-count=2

为了方便测试，先设置只在

t_order0

表上进行测试

xxx.actual-data-nodes=server-order$->{0..1}.t_order0

测试：可以分别测试行表达式分片算法和取模分片算法

/**
     * 水平分片：分库插入数据测试
     */@TestpublicvoidtestInsertOrderDatabaseStrategy(){for(long i =0; i <4; i++){Order order =newOrder();
        order.setOrderNo("20230821001");
        order.setUserId(i +1);
        order.setAmount(newBigDecimal(100));
        orderMapper.insert(order);}}

水平分表：

分片规则：order表中

order_no的哈希值为偶数时

，数据插入对应服务器的

t_order0表

，

order_no的哈希值为奇数时

，数据插入对应服务器的

t_order1表

。因为order_no是字符串形式，因此不能直接取模。

#------------------------分表策略
# 分片列名称
spring.shardingsphere.rules.sharding.tables.t_order.table-strategy.standard.sharding-column=order_no
# 分片算法名称
spring.shardingsphere.rules.sharding.tables.t_order.table-strategy.standard.sharding-algorithm-name=alg_hash_mod

#------------------------分片算法配置
# 哈希取模分片算法
# 分片算法类型
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_hash_mod.type=HASH_MOD
# 分片算法属性配置
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_hash_mod.props.sharding-count=2

测试前不要忘记将如下节点改回原来的状态

xxx.actual-data-nodes=server-order$->{0..1}.t_order$->{0..1}

测试：

/**
     * 水平分片：分表插入数据测试
     */@TestpublicvoidtestInsertOrderTableStrategy(){for(long i =1; i <5; i++){Order order =newOrder();
        order.setOrderNo("gaogzhen"+ i);
        order.setUserId(1L);
        order.setAmount(newBigDecimal(100));
        orderMapper.insert(order);}for(long i =5; i <9; i++){Order order =newOrder();
        order.setOrderNo("gaogzhen"+ i);
        order.setUserId(2L);
        order.setAmount(newBigDecimal(100));
        orderMapper.insert(order);}}/**
     * 测试哈希取模
     */@TestpublicvoidtestHash(){//注意hash取模的结果是整个字符串hash后再取模，和数值后缀是奇数还是偶数无关System.out.println("gaogzhen001".hashCode()%2);System.out.println("gaogzhen0011".hashCode()%2);}

查询测试：

/**
     * 水平分片：查询所有记录
     * 查询了两个数据源，每个数据源中使用UNION ALL连接两个表
     */@TestpublicvoidtestShardingSelectAll(){List<Order> orders = orderMapper.selectList(null);
    orders.forEach(System.out::println);}/**
     * 水平分片：根据user_id查询记录
     * 查询了一个数据源，每个数据源中使用UNION ALL连接两个表
     */@TestpublicvoidtestShardingSelectByUserId(){QueryWrapper<Order> orderQueryWrapper =newQueryWrapper<>();
    orderQueryWrapper.eq("user_id",1L);List<Order> orders = orderMapper.selectList(orderQueryWrapper);
    orders.forEach(System.out::println);}

2.6、分布式序列算法

雪花算法：

https://shardingsphere.apache.org/document/5.1.1/cn/features/sharding/concept/key-generator/

水平分片需要关注全局序列，因为不能简单的使用基于数据库的主键自增。

这里有两种方案：一种是基于MyBatisPlus的id策略；一种是ShardingSphere-JDBC的全局序列配置。

基于MyBatisPlus的id策略：

将Order类的id设置成如下形式

@TableId(type =IdType.ASSIGN_ID)privateLong id;

基于ShardingSphere-JDBC的全局序列配置

：和前面的MyBatisPlus的策略二选一

#------------------------分布式序列策略配置
# 分布式序列列名称
spring.shardingsphere.rules.sharding.tables.t_order.key-generate-strategy.column=id
# 分布式序列算法名称
spring.shardingsphere.rules.sharding.tables.t_order.key-generate-strategy.key-generator-name=alg_snowflake

# 分布式序列算法配置
# 分布式序列算法类型
spring.shardingsphere.rules.sharding.key-generators.alg_snowflake.type=SNOWFLAKE
# 分布式序列算法属性配置
#spring.shardingsphere.rules.sharding.key-generators.alg_snowflake.props.xxx=

此时，需要将实体类中的id策略修改成以下形式：

//当配置了shardingsphere-jdbc的分布式序列时，自动使用shardingsphere-jdbc的分布式序列//当没有配置shardingsphere-jdbc的分布式序列时，自动依赖数据库的主键自增策略@TableId(type =IdType.AUTO)

3、多表关联

3.1、创建关联表

在

server-order0、server-order1

服务器中分别创建两张订单详情表

t_order_item0、t_order_item1

我们希望

同一个用户的订单表和订单详情表中的数据都在同一个数据源中，避免跨库关联

，因此这两张表我们使用相同的分片策略。

那么在

t_order_item

中我们也需要创建

order_no

和

user_id

这两个分片键

CREATETABLE t_order_item0(
    id BIGINT,
    order_no VARCHAR(30),
    user_id BIGINT,
    price DECIMAL(10,2),`count`INT,PRIMARYKEY(id));CREATETABLE t_order_item1(
    id BIGINT,
    order_no VARCHAR(30),
    user_id BIGINT,
    price DECIMAL(10,2),`count`INT,PRIMARYKEY(id));

3.2、创建实体类

packagecom.gaogzhen.shardingjdbcdemo.entity;@TableName("t_order_item")@DatapublicclassOrderItem{//当配置了shardingsphere-jdbc的分布式序列时，自动使用shardingsphere-jdbc的分布式序列@TableId(type =IdType.AUTO)privateLong id;privateString orderNo;privateLong userId;privateBigDecimal price;privateInteger count;}

3.3、创建Mapper

packagecom.gaogzhen.shargingjdbcdemo.mapper;@MapperpublicinterfaceOrderItemMapperextendsBaseMapper<OrderItem>{}

3.4、配置关联表

t_order_item的分片表、分片策略、分布式序列策略和t_order一致

#------------------------标准分片表配置（数据节点配置）
spring.shardingsphere.rules.sharding.tables.t_order_item.actual-data-nodes=server-order$->{0..1}.t_order_item$->{0..1}

#------------------------分库策略
# 分片列名称
spring.shardingsphere.rules.sharding.tables.t_order_item.database-strategy.standard.sharding-column=user_id
# 分片算法名称
spring.shardingsphere.rules.sharding.tables.t_order_item.database-strategy.standard.sharding-algorithm-name=alg_mod

#------------------------分表策略
# 分片列名称
spring.shardingsphere.rules.sharding.tables.t_order_item.table-strategy.standard.sharding-column=order_no
# 分片算法名称
spring.shardingsphere.rules.sharding.tables.t_order_item.table-strategy.standard.sharding-algorithm-name=alg_hash_mod

#------------------------分布式序列策略配置
# 分布式序列列名称
spring.shardingsphere.rules.sharding.tables.t_order_item.key-generate-strategy.column=id
# 分布式序列算法名称
spring.shardingsphere.rules.sharding.tables.t_order_item.key-generate-strategy.key-generator-name=alg_snowflake

3.5、测试插入数据

同一个用户的订单表和订单详情表中的数据都在同一个数据源中，避免跨库关联

/**
     * 测试关联表插入
     */@TestpublicvoidtestInsertOrderAndOrderItem(){for(long i =1; i <3; i++){Order order =newOrder();
        order.setOrderNo("gaogzhen"+ i);
        order.setUserId(1L);
        orderMapper.insert(order);for(long j =1; j <3; j++){OrderItem orderItem =newOrderItem();
            orderItem.setOrderNo("gaogzhen"+ i);
            orderItem.setUserId(1L);
            orderItem.setPrice(newBigDecimal(10));
            orderItem.setCount(2);
            orderItemMapper.insert(orderItem);}}for(long i =5; i <7; i++){Order order =newOrder();
        order.setOrderNo("gaogzhen"+ i);
        order.setUserId(2L);
        orderMapper.insert(order);for(long j =1; j <3; j++){OrderItem orderItem =newOrderItem();
            orderItem.setOrderNo("gaogzhen"+ i);
            orderItem.setUserId(2L);
            orderItem.setPrice(newBigDecimal(1));
            orderItem.setCount(3);
            orderItemMapper.insert(orderItem);}}}

4、绑定表

需求：查询每个订单的订单号和总订单金额

4.1、创建VO对象

packagecom.gaogzhen.shardingjdbcdemo.entity;@DatapublicclassOrderVo{privateString orderNo;privateBigDecimal amount;}

4.2、添加Mapper方法

OrderMapper.java

packagecom.gaogzhen.shardingjdbcdemo.mapper;@MapperpublicinterfaceOrderMapperextendsBaseMapper<Order>{/**
     * 计算订单金额
     * @return 订单金额列表
     */List<OrderVO>getOrderAmount();}

OrderMapper.xml<selectid="getOrderAmount"resultType="com.gaogzhen.shardingjdbcdemo.vo.OrderVO"> select t1.order_no, sum(t2.price * t2.count) amount from t_order t1 join t_order_item t2 on t2.order_no = t1.order_no group by t1.order_no </select>

4.3、测试关联查询

/**
     * 测试关联表查询
     */@TestpublicvoidtestGetOrderAmount(){List<OrderVo> orderAmountList = orderMapper.getOrderAmount();
    orderAmountList.forEach(System.out::println);}

查询结果

2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Logic SQL: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order t1
            join t_order_item t2 on t2.order_no = t1.order_no
        group by
            t1.order_no
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : SQLStatement: MySQLSelectStatement(table=Optional.empty, limit=Optional.empty, lock=Optional.empty, window=Optional.empty)
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Actual SQL: server-order1 ::: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order0 t1
            join t_order_item0 t2 on t2.order_no = t1.order_no
        group by
            t1.order_no ORDER BY t1.order_no ASC 
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Actual SQL: server-order1 ::: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order1 t1
            join t_order_item0 t2 on t2.order_no = t1.order_no
        group by
            t1.order_no ORDER BY t1.order_no ASC 
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Actual SQL: server-order1 ::: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order0 t1
            join t_order_item1 t2 on t2.order_no = t1.order_no
        group by
            t1.order_no ORDER BY t1.order_no ASC 
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Actual SQL: server-order1 ::: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order1 t1
            join t_order_item1 t2 on t2.order_no = t1.order_no
        group by
            t1.order_no ORDER BY t1.order_no ASC 
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Actual SQL: server-order0 ::: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order0 t1
            join t_order_item0 t2 on t2.order_no = t1.order_no
        group by
            t1.order_no ORDER BY t1.order_no ASC 
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Actual SQL: server-order0 ::: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order1 t1
            join t_order_item0 t2 on t2.order_no = t1.order_no
        group by
            t1.order_no ORDER BY t1.order_no ASC 
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Actual SQL: server-order0 ::: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order0 t1
            join t_order_item1 t2 on t2.order_no = t1.order_no
        group by
            t1.order_no ORDER BY t1.order_no ASC 
2023-08-23 20:10:40.015  INFO 27448 --- [           main] ShardingSphere-SQL                       : Actual SQL: server-order0 ::: select
            t1.order_no,
            sum(t2.price * t2.count) amount
        from
            t_order1 t1
            join t_order_item1 t2 on t2.order_no = t1.order_no
        group by
            t1.order_no ORDER BY t1.order_no ASC 
OrderVO(orderNo=gaogzhen1, amount=40.00)
OrderVO(orderNo=gaogzhen2, amount=40.00)
OrderVO(orderNo=gaogzhen5, amount=6.00)
OrderVO(orderNo=gaogzhen6, amount=6.00)

4.4、配置绑定表

在原来水平分片配置的基础上添加如下配置：

#------------------------绑定表
spring.shardingsphere.rules.sharding.binding-tables[0]=t_order,t_order_item

配置完绑定表后再次进行关联查询的测试：

如果不配置绑定表：测试的结果为8个SQL。多表关联查询会出现笛卡尔积关联。
如果配置绑定表：测试的结果为4个SQL。 多表关联查询不会出现笛卡尔积关联，关联查询效率将大大提升。

绑定表：

指分片规则一致的一组分片表。使用绑定表进行多表关联查询时，必须使用分片键进行关联，否则会出现笛卡尔积关联或跨库关联，从而影响查询效率。

目前测试还是查询8个SQL，配置未生效，暂时没找到解决方法

5、广播表

4.1、什么是广播表

指所有的分片数据源中都存在的表，表结构及其数据在每个数据库中均完全一致。适用于数据量不大且需要与海量数据的表进行关联查询的场景，例如：字典表。

广播具有以下特性：

（1）插入、更新操作会实时在所有节点上执行，保持各个分片的数据一致性

（2）查询操作，只从一个节点获取

（3）可以跟任何一个表进行 JOIN 操作

4.2、创建广播表

在server-order0、server-order1和server-user服务器中分别创建t_dict表

CREATETABLE t_dict(
    id BIGINT,
    dict_type VARCHAR(200),PRIMARYKEY(id));

4.3、程序实现

4.3.1、创建实体类

packagecom.gaogzhen.shardingjdbcdemo.entity;@TableName("t_dict")@DatapublicclassDict{//可以使用MyBatisPlus的雪花算法@TableId(type =IdType.ASSIGN_ID)privateLong id;privateString dictType;}

4.3.2、创建Mapper

packagecom.gaogzhen.shardingjdbcdemo.mapper;@MapperpublicinterfaceDictMapperextendsBaseMapper<Dict>{}

4.3.3、配置广播表

#数据节点可不配置，默认情况下，向所有数据源广播
spring.shardingsphere.rules.sharding.tables.t_dict.actual-data-nodes=server-user.t_dict,server-order$->{0..1}.t_dict

# 广播表
spring.shardingsphere.rules.sharding.broadcast-tables[0]=t_dict

4.4、测试广播表

@AutowiredprivateDictMapper dictMapper;/**
     * 广播表：每个服务器中的t_dict同时添加了新数据
     */@TestpublicvoidtestBroadcast(){Dict dict =newDict();
    dict.setDictType("type1");
    dictMapper.insert(dict);}/**
     * 查询操作，只从一个节点获取数据
     * 随机负载均衡规则
     */@TestpublicvoidtestSelectBroadcast(){List<Dict> dicts = dictMapper.selectList(null);
    dicts.forEach(System.out::println);}

5 配置文件方式

application.properties

#----------------------- 基础配置
# 项目名称
spring.application.name=sharding-jdbc-demo
spring.profiles.active=dev
# shardingsphere 配置
# 模式
spring.shardingsphere.mode.type=Memory

# 数据源名称
spring.shardingsphere.datasource.names=server-user,server-order0,server-order1

#------------------------ 数据源配置
# 配置第1个数据源
spring.shardingsphere.datasource.server-user.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.server-user.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.server-user.jdbc-url=jdbc:mysql://127.0.0.1:3301/db_user?allowPublicKeyRetrieval=true&useSSL=false
spring.shardingsphere.datasource.server-user.username=root
spring.shardingsphere.datasource.server-user.password=123456

# 配置第2个数据源
spring.shardingsphere.datasource.server-order0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.server-order0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.server-order0.jdbc-url=jdbc:mysql://127.0.0.1:3310/db_order?allowPublicKeyRetrieval=true&useSSL=false
spring.shardingsphere.datasource.server-order0.username=root
spring.shardingsphere.datasource.server-order0.password=123456

# 配置第3个数据源
spring.shardingsphere.datasource.server-order1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.server-order1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.server-order1.jdbc-url=jdbc:mysql://127.0.0.1:3311/db_order?allowPublicKeyRetrieval=true&useSSL=false
spring.shardingsphere.datasource.server-order1.username=root
spring.shardingsphere.datasource.server-order1.password=123456

#------------------------数据节点配置
## 标准分配表配置
# spring.shardingsphere.rules.sharding.tables.<table-name>.actual-data-nodes=值
# 值由数据源名 + 表名组成，以小数点分隔。多个表以逗号分隔，支持 inline 表达式。
# <table-name>：逻辑表名
spring.shardingsphere.rules.sharding.tables.t_user.actual-data-nodes=server-user.t_user
spring.shardingsphere.rules.sharding.tables.t_order.actual-data-nodes=server-order$->{0..1}.t_order$->{0..1}
#spring.shardingsphere.rules.sharding.tables.t_order.actual-data-nodes=server-order$->{0..1}.t_order0

#------------------------分库策略
# 分片列配置
spring.shardingsphere.rules.sharding.tables.t_order.database-strategy.standard.sharding-column=user_id
# 分片算法名称
spring.shardingsphere.rules.sharding.tables.t_order.database-strategy.standard.sharding-algorithm-name=alg_inline_userid

#------------------------分片算法配置
# 行表达式分片算法
# 分片算法类型
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_inline_userid.type=INLINE
# 分片算法属性配置
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_inline_userid.props.algorithm-expression=server-order$->{user_id % 2}

# 取模分片算法
# 分片算法类型
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_mod.type=MOD
# 分片算法属性配置
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_mod.props.sharding-count=2

#------------------------分表策略
# 分片列名称
spring.shardingsphere.rules.sharding.tables.t_order.table-strategy.standard.sharding-column=order_no
# 分片算法名称
spring.shardingsphere.rules.sharding.tables.t_order.table-strategy.standard.sharding-algorithm-name=alg_hash_mod

#------------------------分片算法配置
# 哈希取模分片算法
# 分片算法类型
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_hash_mod.type=HASH_MOD
# 分片算法属性配置
spring.shardingsphere.rules.sharding.sharding-algorithms.alg_hash_mod.props.sharding-count=2

#------------------------分布式序列策略配置
# 分布式序列列名称
spring.shardingsphere.rules.sharding.tables.t_order.key-generate-strategy.column=id
# 分布式序列算法名称
spring.shardingsphere.rules.sharding.tables.t_order.key-generate-strategy.key-generator-name=alg_snowflake

# 分布式序列算法配置
# 分布式序列算法类型
spring.shardingsphere.rules.sharding.key-generators.alg_snowflake.type=SNOWFLAKE
# 分布式序列算法属性配置
#spring.shardingsphere.rules.sharding.key-generators.alg_snowflake.props.xxx=

#------------------------标准分片表配置（数据节点配置）
spring.shardingsphere.rules.sharding.tables.t_order_item.actual-data-nodes=server-order$->{0..1}.t_order_item$->{0..1}

#------------------------分库策略
# 分片列名称
spring.shardingsphere.rules.sharding.tables.t_order_item.database-strategy.standard.sharding-column=user_id
# 分片算法名称
spring.shardingsphere.rules.sharding.tables.t_order_item.database-strategy.standard.sharding-algorithm-name=alg_mod

#------------------------分表策略
# 分片列名称
spring.shardingsphere.rules.sharding.tables.t_order_item.table-strategy.standard.sharding-column=order_no
# 分片算法名称
spring.shardingsphere.rules.sharding.tables.t_order_item.table-strategy.standard.sharding-algorithm-name=alg_hash_mod

#------------------------分布式序列策略配置
# 分布式序列列名称
spring.shardingsphere.rules.sharding.tables.t_order_item.key-generate-strategy.column=id
# 分布式序列算法名称
spring.shardingsphere.rules.sharding.tables.t_order_item.key-generate-strategy.key-generator-name=alg_snowflake

#------------------------绑定表
spring.shardingsphere.rules.sharding.binding-tables=t_order,t_order_item

# 广播表
spring.shardingsphere.rules.sharding.broadcast-tables[0]=t_dict

# 打印日志
spring.shardingsphere.props.sql-show=true

# mybatis plus 配置
mybatis.mapper-locations=classpath:mapper/*.xml
mybatis.type-aliases-package=com.gaogzhen.shardingjdbcdemo.entity

application.properties+applicaton-dev.yml#----------------------- 基础配置# 项目名称spring.application.name=sharding-jdbc-demospring.profiles.active=dev# mybatis plus 配置mybatis.mapper-locations=classpath:mapper/*.xmlmybatis.type-aliases-package=com.gaogzhen.shardingjdbcdemo.entity``````spring:shardingSphere:mode:type: Memory schema:name: horizontal-sharding datasource:names: server_user,server-order0,server-order1 server-user:type: com.zaxxer.hikari.HikariDataSource driverClassName: com.mysql.jdbc.Driver url: jdbc:mysql://127.0.0.1:3301/db_user?allowPublicKeyRetrieval=true&useSSL=falseusername: root password:123456server-order0:type: com.zaxxer.hikari.HikariDataSource driverClassName: com.mysql.jdbc.Driver url: jdbc:mysql://127.0.0.1:3310/db_order?allowPublicKeyRetrieval=true&useSSL=falseusername: root password:123456server-order1:type: com.zaxxer.hikari.HikariDataSource driverClassName: com.mysql.jdbc.Driver url: jdbc:mysql://127.0.0.1:3311/db_order?allowPublicKeyRetrieval=true&useSSL=falseusername: root password:123456rules:sharding:tables:t_user:actualDataNodes: server-user.t_user t_order:actualDataNodes: server-order$->{0..1}.t_order$->{0..1}databaseStrategy:standard:shardingColumn: user_id shardingAlgorithmName: alg-inline-userid tableStrategy:standard:shardingColumn: order_no shardingAlgorithmName: alg-hash-mod keyGenerateStrategy:column: id keyGeneratorName: alg-snowflake t_order_item:actualDataNodes: server-order$->{0..1}.t_order_item$->{0..1}databaseStrategy:standard:shardingColumn: user_id shardingAlgorithmName: alg-mod tableStrategy:standard:shardingColumn: order_no shardingAlgorithmName: alg-hash-mod keyGenerateStrategy:column: id keyGeneratorName: alg-snowflake keyGenerators:alg-snowflake:type: SNOWFLAKE shardingAlgorithms:alg-inline-userid:type: INLINE props:algorithm-expression: server-order$->{user_id % 2}alg-mod:type: MOD props:sharding-count:2alg-hash-mod:type: HASH_MOD props:sharding-count:2binding-tables: t_order,t_order_item broadcast-tables: t_dict props:sqlShow:true

6 问题集

6.1 简述

sharding-jdbc 报错多半报错因为配置文件引起的，除了个人粗心大意外，多半和官方给的配置字段名有关。官方文档配置字段名有的给驼峰形式，有的给”-“连接形式，这里建议统一用”-"连接的形式。

props下的所有配置需要使用"-"连接的形式，不然报错或者不生效

6.1 Parameter index out of range

报错内容如下：

### Error updating database.  Cause: java.sql.SQLException: Parameter index out of range (1 > number of parameters, which is 0).
### The error may exist in com/gaogzhen/shardingjdbcdemo/mapper/OrderItemMapper.java (best guess)
### The error may involve com.gaogzhen.shardingjdbcdemo.mapper.OrderItemMapper.insert-Inline
### The error occurred while setting parameters
### SQL: INSERT INTO t_order_item0  ( order_no, user_id, price, count )  VALUES  ( ?, ?, ?, ? )
### Cause: java.sql.SQLException: Parameter index out of range (1 > number of parameters, which is 0).
; Parameter index out of range (1 > number of parameters, which is 0).; nested exception is java.sql.SQLException: Parameter index out of range (1 > number of parameters, which is 0).
    at com.gaogzhen.shardingjdbcdemo.HorizontalShardingTest.testInsertOrderAndOrderItem(HorizontalShardingTest.java:102)
Caused by: java.sql.SQLException: Parameter index out of range (1 > number of parameters, which is 0).
    at com.gaogzhen.shardingjdbcdemo.HorizontalShardingTest.testInsertOrderAndOrderItem(HorizontalShardingTest.java:102)

可能出现问题原因

首选确保官网文档固定的配置项不出现错误，比如table-strategy 大小写，下划线或驼峰形式
对于自定义的数据源名称、逻辑表名称注意前后一致
然后MybatisPlus实体类表名注解@TableName(value =“t_order_item”) 其中表名为配置的逻辑表名，非真实表名

6.2 No implementation class load from SPI

报错内容：

org.apache.shardingsphere.spi.exception.ServiceProviderNotFoundException: No implementation class load from SPI `org.apache.shardingsphere.sharding.spi.ShardingAlgorithm` with type `null`.

6.3 Error creating bean with name ‘org.apache.shardingsphere.spring.boot.ShardingSphereAutoConfiguration’

报错内容：

java.lang.IllegalStateException: Failed to load ApplicationContext
Caused by: org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'org.apache.shardingsphere.spring.boot.ShardingSphereAutoConfiguration': Initialization of bean failed; nested exception is java.lang.NullPointerException
Caused by: java.lang.NullPointerException

出错原因# 按照官网文档配置的数据源如下datasource:names: server_user,server_order0,server_order1 server_user:dataSourceClassName: com.zaxxer.hikari.HikariDataSource driverClassName: com.mysql.jdbc.Driver jdbcUrl: jdbc:mysql://127.0.0.1:3301/db_user?allowPublicKeyRetrieval=true&useSSL=falseusername: root password:123456
解决方案：datasource:names: server_user,server_order0,server_order1 server_user:type: com.zaxxer.hikari.HikariDataSource driverClassName: com.mysql.jdbc.Driver url: jdbc:mysql://127.0.0.1:3301/db_user?allowPublicKeyRetrieval=true&useSSL=falseusername: root password:123456- dataSourceClassName替换为type- jdbcUrl替换为url

6.4 could not determine a constructor for the tag !SHARDING:

报错内容

java.lang.IllegalStateException: Failed to load ApplicationContext
Caused by: org.yaml.snakeyaml.constructor.ConstructorException: 
could not determine a constructor for the tag !SHARDING:
 in 'reader', line 28, column 7:
        - !SHARDING:

我的shardingsphere 版本5.1.1 按照官网sharding-jdbc yaml配置会报上述错误，不识别- !SHARDING

解决方案把-!SHARDING替换为sharding如下图所示：

6.5 Data sources cannot be empty

报错内容Caused by: org.springframework.beans.BeanInstantiationException: Failed to instantiate [javax.sql.DataSource]: Factory method 'shardingSphereDataSource' threw exception; nested exception is java.lang.IllegalArgumentException: Data sources cannot be empty.Caused by: java.lang.IllegalArgumentException: Data sources cannot be empty.
出错原因# 按照官网文档配置dataSources:server_user:dataSourceClassName: com.zaxxer.hikari.HikariDataSource driverClassName: com.mysql.jdbc.Driver jdbcUrl: jdbc:mysql://127.0.0.1:3301/db_user?allowPublicKeyRetrieval=true&useSSL=falseusername: root password:123456
解决方案datasource:names: server_user,server_order0,server_order1 server_user:type: com.zaxxer.hikari.HikariDataSource driverClassName: com.mysql.jdbc.Driver url: jdbc:mysql://127.0.0.1:3301/db_user?allowPublicKeyRetrieval=true&useSSL=falseusername: root password:123456- dataSources替换为datasource- 添加names属性，值为逻辑属性源

6.6 Insert statement does not support sharding table routing to multiple data nodes

报错内容org.mybatis.spring.MyBatisSystemException: nested exception is org.apache.ibatis.exceptions.PersistenceException: ### Error updating database. Cause: java.lang.IllegalStateException: Insert statement does not support sharding table routing to multiple data nodes.### The error may exist in com/gaogzhen/shardingjdbcdemo/mapper/OrderMapper.java (best guess)### The error may involve com.gaogzhen.shardingjdbcdemo.mapper.OrderMapper.insert-Inline### The error occurred while setting parameters### SQL: INSERT INTO t_order ( order_no, user_id ) VALUES ( ?, ? )### Cause: java.lang.IllegalStateException: Insert statement does not support sharding table routing to multiple data nodes. at com.gaogzhen.shardingjdbcdemo.HorizontalShardingTest.testInsertOrderAndOrderItem(HorizontalShardingTest.java:94)Caused by: org.apache.ibatis.exceptions.PersistenceException:
报错原因rules:sharding:tables:t_order_item:actualDataNodes: server-order$->{0..1}.t_order_item$->{0..1}databaseStrategy:standard:shardingColumn: user_id shardingAlgorithmName: alg-mod tableStrategy:standard:shardingColumn: order_id shardingAlgorithmName: alg-hash-mod keyGenerateStrategy:column: id keyGeneratorName: alg-snowflake keyGenerators:alg-snowflake:type: SNOWFLAKE shardingAlgorithms:alg-inline-userid:type: INLINE props:algorithm-expression: server-order$->{user_id % 2}alg-mod:type: MOD props:sharding-count:2alg-hash-mod:type: HASH_MOD props:sharding-count:2- 分库或者分表算法名称不能使用“_"下划线分割，用“-”代替### 6.7 Inline sharding algorithm expression cannot be null or empty- 报错原因shardingAlgorithms:alg-inline-userid:type: INLINE props:algorithmExpression: server-order$->{user_id % 2}- algorithmExpression不能为驼峰命名- 解决方案algorithmExpression改为algorithm-expression

结语

如果小伙伴什么问题或者指教，欢迎交流。

QQ:806797785

仓库源代码地址：https://gitee.com/gaogzhen/shardingsphere-jdbc-demo.git

参考链接:

[1]ShardingSphere5实战教程[CP/OL].2022-09-14.p18-23.

[2]0101读写分离测试-jdbc-shardingsphere-中间件[CP/OL].

[3]0102垂直分片-jdbc-shardingsphere[CP/OL].

标签：中间件分布式数据库 shardingshpere

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