Sharding-JDBC之垂直分库水平分表

一、简介

  这里的垂直分库分表是指 垂直分库 + 水平分表 ，怎么解释呢，一般是一个库中同时有订单表和用户表，随着数据增多，就把订单表和用户表单独变成两个库，达到专库专用。当订单库或用户库数据增多，然后分别对订单库和用户库的进行水平分表，一个库多个一样的表。先看下大致架构图：

数据流向图如下：

二、maven依赖

pom.xml

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0modelVersion>
    <parent>
        <groupId>org.springframework.bootgroupId>
        <artifactId>spring-boot-starter-parentartifactId>
        <version>2.6.0version>
        <relativePath/> 
    parent>
    <groupId>com.aliangroupId>
    <artifactId>sharding-jdbc-vertical-databaseartifactId>
    <version>0.0.1-SNAPSHOTversion>
    <name>sharding-jdbc-vertical-databasename>
    <description>sharding-jdbc-vertical-databasedescription>

    <properties>
        <java.version>1.8java.version>
    properties>

    <dependencies>
        <dependency>
            <groupId>org.springframework.bootgroupId>
            <artifactId>spring-boot-starter-webartifactId>
        dependency>

        <dependency>
            <groupId>org.springframework.bootgroupId>
            <artifactId>spring-boot-starter-data-jpaartifactId>
        dependency>

        <dependency>
            <groupId>org.apache.shardingspheregroupId>
            <artifactId>sharding-jdbc-spring-boot-starterartifactId>
            <version>4.1.1version>
        dependency>

        <dependency>
            <groupId>com.alibabagroupId>
            <artifactId>druidartifactId>
            <version>1.2.15version>
        dependency>

        <dependency>
            <groupId>mysqlgroupId>
            <artifactId>mysql-connector-javaartifactId>
            <version>8.0.26version>
            <scope>runtimescope>
        dependency>

        <dependency>
            <groupId>org.springframework.bootgroupId>
            <artifactId>spring-boot-starter-testartifactId>
            <scope>testscope>
        dependency>

        <dependency>
            <groupId>org.projectlombokgroupId>
            <artifactId>lombokartifactId>
            <version>1.18.20version>
        dependency>

        <dependency>
            <groupId>junitgroupId>
            <artifactId>junitartifactId>
            <version>4.12version>
            <scope>testscope>
        dependency>

    dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.springframework.bootgroupId>
                <artifactId>spring-boot-maven-pluginartifactId>
            plugin>
        plugins>
    build>

project>

  有些小伙伴的 druid 可能用的是 druid-spring-boot-starter

<dependency>
    <groupId>com.alibabagroupId>
    <artifactId>druid-spring-boot-starterartifactId>
    <version>1.2.6version>
dependency>

  然后出现可能使用不了的各种问题，这个时候你只需要在主类上添加 @SpringBootApplication(exclude = {DruidDataSourceAutoConfigure.class}) 即可

package com.alian.shardingjdbc;

import com.alibaba.druid.spring.boot.autoconfigure.DruidDataSourceAutoConfigure;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication(exclude = {DruidDataSourceAutoConfigure.class})
@SpringBootApplication
public class ShardingJdbcApplication {

    public static void main(String[] args) {
        SpringApplication.run(ShardingJdbcApplication.class, args);
    }

}

三、数据库

3.1、创建数据库

CREATE DATABASE `sharding_3` DEFAULT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci;
CREATE DATABASE `sharding_4` DEFAULT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci;

3.2、订单表

  在数据库sharding_3创建两张表：tb_order_1和tb_order_2，表的结构都是一样的。

tb_order_1

CREATE TABLE `tb_order_1` (
  `order_id` bigint(20) NOT NULL COMMENT '主键',
  `user_id` int unsigned NOT NULL DEFAULT '0' COMMENT '用户id',
  `price` int unsigned NOT NULL DEFAULT '0' COMMENT '价格（单位：分）',
  `order_status` tinyint unsigned NOT NULL DEFAULT '1' COMMENT '订单状态(1:待付款,2:已付款,3:已取消)',
  `order_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `title` varchar(100)  NOT NULL DEFAULT '' COMMENT '订单标题',
  PRIMARY KEY (`order_id`),
  KEY `idx_user_id` (`user_id`),
  KEY `idx_order_time` (`order_time`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='订单表';

tb_order_2

CREATE TABLE `tb_order_2` (
  `order_id` bigint(20) NOT NULL COMMENT '主键',
  `user_id` int unsigned NOT NULL DEFAULT '0' COMMENT '用户id',
  `price` int unsigned NOT NULL DEFAULT '0' COMMENT '价格（单位：分）',
  `order_status` tinyint unsigned NOT NULL DEFAULT '1' COMMENT '订单状态(1:待付款,2:已付款,3:已取消)',
  `order_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `title` varchar(100)  NOT NULL DEFAULT '' COMMENT '订单标题',
  PRIMARY KEY (`order_id`),
  KEY `idx_user_id` (`user_id`),
  KEY `idx_order_time` (`order_time`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='订单表';

3.3、用户表

  在数据库sharding_4创建两张表：tb_user_1和tb_user_2，表的结构都是一样的。

CREATE TABLE `tb_user_1` (
  `id` bigint(20) NOT NULL COMMENT '主键',
  `user_name` VARCHAR(20) NOT NULL DEFAULT '' COMMENT '用户姓名',
  `age` tinyint unsigned NOT NULL DEFAULT '0' COMMENT '年龄',
  `create_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `update_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间',
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='用户表';

CREATE TABLE `tb_user_2` (
  `id` bigint(20) NOT NULL COMMENT '主键',
  `user_name` VARCHAR(20) NOT NULL DEFAULT '' COMMENT '用户姓名',
  `age` tinyint unsigned NOT NULL DEFAULT '0' COMMENT '年龄',
  `create_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `update_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间',
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='用户表';

四、配置（二选一）

4.1、properties配置

application.properties

server.port=8899
server.servlet.context-path=/sharding-jdbc

# 允许定义相同的bean对象去覆盖原有的
spring.main.allow-bean-definition-overriding=true
# 数据源名称，多数据源以逗号分隔
spring.shardingsphere.datasource.names=ds1,ds2
# sharding_1数据库连接池类名称
spring.shardingsphere.datasource.ds1.type=com.alibaba.druid.pool.DruidDataSource
# sharding_1数据库驱动类名
spring.shardingsphere.datasource.ds1.driver-class-name=com.mysql.cj.jdbc.Driver
# sharding_1数据库url连接
spring.shardingsphere.datasource.ds1.url=jdbc:mysql://192.168.19.129:3306/sharding_3?serverTimezone=GMT%2B8&characterEncoding=utf8&useUnicode=true&useSSL=false&zeroDateTimeBehavior=CONVERT_TO_NULL&autoReconnect=true&allowMultiQueries=true&failOverReadOnly=false&connectTimeout=6000&maxReconnects=5
# sharding_1数据库用户名
spring.shardingsphere.datasource.ds1.username=alian
# sharding_1数据库密码
spring.shardingsphere.datasource.ds1.password=123456

# sharding_2数据库连接池类名称
spring.shardingsphere.datasource.ds2.type=com.alibaba.druid.pool.DruidDataSource
# sharding_2数据库驱动类名
spring.shardingsphere.datasource.ds2.driver-class-name=com.mysql.cj.jdbc.Driver
# sharding_2数据库url连接
spring.shardingsphere.datasource.ds2.url=jdbc:mysql://192.168.19.130:3306/sharding_4?serverTimezone=GMT%2B8&characterEncoding=utf8&useUnicode=true&useSSL=false&zeroDateTimeBehavior=CONVERT_TO_NULL&autoReconnect=true&allowMultiQueries=true&failOverReadOnly=false&connectTimeout=6000&maxReconnects=5
# sharding_2数据库用户名
spring.shardingsphere.datasource.ds2.username=alian
# sharding_2数据库密码
spring.shardingsphere.datasource.ds2.password=123456

# 指定tb_order表的数据分布情况，配置数据节点，使用Groovy的表达式,逻辑表tb_order对应的节点是：ds1.tb_order_1, ds1.tb_order_2
spring.shardingsphere.sharding.tables.tb_order.actual-data-nodes=ds1.tb_order_$->{1..2}
# 采用行表达式分片策略：InlineShardingStrategy
# 指定tb_order表的分片策略中的分片键
spring.shardingsphere.sharding.tables.tb_order.table-strategy.inline.sharding-column=order_id
# 指定tb_order表的分片策略中的分片算法表达式,使用Groovy的表达式
spring.shardingsphere.sharding.tables.tb_order.table-strategy.inline.algorithm-expression=tb_order_$->{order_id%2==0?2:1}
# 指定tb_order表的主键为order_id
spring.shardingsphere.sharding.tables.tb_order.key-generator.column=order_id
# 指定tb_order表的主键生成策略为SNOWFLAKE
spring.shardingsphere.sharding.tables.tb_order.key-generator.type=SNOWFLAKE
# 指定雪花算法的worker.id
spring.shardingsphere.sharding.tables.tb_order.key-generator.props.worker.id=100
# 指定雪花算法的max.tolerate.time.difference.milliseconds
spring.shardingsphere.sharding.tables.tb_order.key-generator.props.max.tolerate.time.difference.milliseconds=20

# 指定tb_user表的数据分布情况，配置数据节点，使用Groovy的表达式,逻辑表tb_user对应的节点是：ds2.tb_user_1, ds2.tb_user_2
spring.shardingsphere.sharding.tables.tb_user.actual-data-nodes=ds2.tb_user_$->{1..2}
# 采用行表达式分片策略：InlineShardingStrategy
# 指定tb_user表的分片策略中的分片键
spring.shardingsphere.sharding.tables.tb_user.table-strategy.inline.sharding-column=id
# 指定tb_user表的分片策略中的分片算法表达式,使用Groovy的表达式
spring.shardingsphere.sharding.tables.tb_user.table-strategy.inline.algorithm-expression=tb_user_$->{id%2==0?2:1}
# 指定tb_user表的主键为order_id
spring.shardingsphere.sharding.tables.tb_user.key-generator.column=id
# 指定tb_user表的主键生成策略为SNOWFLAKE
spring.shardingsphere.sharding.tables.tb_user.key-generator.type=SNOWFLAKE
# 指定雪花算法的worker.id
spring.shardingsphere.sharding.tables.tb_order.key-generator.props.worker.id=101
# 指定雪花算法的max.tolerate.time.difference.milliseconds
spring.shardingsphere.sharding.tables.tb_order.key-generator.props.max.tolerate.time.difference.milliseconds=20

# 打开sql输出日志
spring.shardingsphere.props.sql.show=true

4.2、yml配置

application.yml

server:
  port: 8899
  servlet:
    context-path: /sharding-jdbc

spring:
  main:
    # 允许定义相同的bean对象去覆盖原有的
    allow-bean-definition-overriding: true
  shardingsphere:
    props:
      sql:
       # 打开sql输出日志
       show: true
    datasource:
      # 数据源名称，多数据源以逗号分隔
      names: ds1,ds2
      ds1:
        # 数据库连接池类名称
        type: com.alibaba.druid.pool.DruidDataSource
        # 数据库驱动类名
        driver-class-name: com.mysql.cj.jdbc.Driver
        # 数据库url连接
        url: jdbc:mysql://192.168.19.129:3306/sharding_3?serverTimezone=GMT%2B8&characterEncoding=utf8&useUnicode=true&useSSL=false&zeroDateTimeBehavior=CONVERT_TO_NULL&autoReconnect=true&allowMultiQueries=true&failOverReadOnly=false&connectTimeout=6000&maxReconnects=5
        # 数据库用户名
        username: alian
        # 数据库密码
        password: 123456
      ds2:
        # 数据库连接池类名称
        type: com.alibaba.druid.pool.DruidDataSource
        # 数据库驱动类名
        driver-class-name: com.mysql.cj.jdbc.Driver
        # 数据库url连接
        url: jdbc:mysql://192.168.19.130:3306/sharding_4?serverTimezone=GMT%2B8&characterEncoding=utf8&useUnicode=true&useSSL=false&zeroDateTimeBehavior=CONVERT_TO_NULL&autoReconnect=true&allowMultiQueries=true&failOverReadOnly=false&connectTimeout=6000&maxReconnects=5
        # 数据库用户名
        username: alian
        # 数据库密码
        password: 123456
    sharding:
      # 未配置分片规则的表将通过默认数据源定位
      default-data-source-name: ds1
      tables:
        tb_order:
          # 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
          actual-data-nodes: ds1.tb_order_$->{1..2}
          # 分表策略
          table-strategy:
            # 行表达式分片策略
            inline:
              # 分片键
              sharding-column: order_id
              # 算法表达式
              algorithm-expression: tb_order_$->{order_id%2==0?2:1}
          # key生成器
          key-generator:
            # 自增列名称，缺省表示不使用自增主键生成器
            column: order_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（SNOWFLAKE/UUID）
            type: SNOWFLAKE
            # SnowflakeShardingKeyGenerator
            props:
              # SNOWFLAKE算法的worker.id
              worker:
                id: 100
              # SNOWFLAKE算法的max.tolerate.time.difference.milliseconds
              max:
                tolerate:
                  time:
                    difference:
                      milliseconds: 20
        tb_user:
          # 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
          actual-data-nodes: ds2.tb_user_$->{1..2}
          # 分表策略
          table-strategy:
            # 行表达式分片策略
            inline:
              # 分片键
              sharding-column: id
              # 算法表达式
              algorithm-expression: tb_user_$->{id%2==0?2:1}
          # key生成器
          key-generator:
            # 自增列名称，缺省表示不使用自增主键生成器
            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（SNOWFLAKE/UUID）
            type: SNOWFLAKE
            # SnowflakeShardingKeyGenerator
            props:
              # SNOWFLAKE算法的worker.id
              worker:
                id: 101
              # SNOWFLAKE算法的max.tolerate.time.difference.milliseconds
              max:
                tolerate:
                  time:
                    difference:
                      milliseconds: 20

• 分库策略，这里采用的是行表达式分片策略，对于订单库order_id为奇数就放到ds1.tb_order_1数据源，order_id为偶数就放到ds1.tb_order_2；对于用户库user_id为奇数就放到ds2.tb_user_1数据源，user_id为偶数就放到ds2.tb_user_2，
• actual-data-nodes ：使用Groovy的表达式 ds1.tb_order_$->{1…2}，表示逻辑表tb_order对应的物理表是：ds1.tb_order_1、 ds1.tb_order_2；使用Groovy的表达式 ds2.tb_user_$->{1…2}，表示逻辑表tb_user对应的物理表是：ds2.tb_user_1、 ds2.tb_user_2
• key-generator ：key生成器，需要指定字段和类型，如果是SNOWFLAKE，最好也配置下props中的两个属性： worker.id 与 max.tolerate.time.difference.milliseconds 属性（主要还是yml中配置）
• table-strategy 表的分片策略，这里只是一个简单的奇数偶数，采用的是 行表达式分片策略 ，需要指定分片键和分片算法表达式（算法支持Groovy的表达式）

五、实现

5.1、实体

Order.java

@Data
@Entity
@Table(name = "tb_order")
public class Order implements Serializable {

    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    @Column(name = "order_id")
    private Long orderId;

    @Column(name = "user_id")
    private Integer userId;

    @Column(name = "price")
    private Integer price;

    @Column(name = "order_status")
    private Integer orderStatus;

    @Column(name = "title")
    private String title;

    @Column(name = "order_time")
    private LocalDateTime orderTime;

}

5.2、持久层

OrderRepository.java

public interface OrderRepository extends PagingAndSortingRepository<Order, Long> {

    /**
     * 根据订单id查询订单
     * @param orderId
     * @return
     */
    Order findOrderByOrderId(Long orderId);
}

UserRepository.java

public interface UserRepository extends PagingAndSortingRepository<User, Long> {

    /**
     * 根据用户id查询订单
     *
     * @param id
     * @return
     */
    User findUserById(Long id);
}

5.3、服务层

OrderService.java

@Slf4j
@Service
public class OrderService {

    @Autowired
    private OrderRepository orderRepository;

    public void saveOrder(Order order) {
        orderRepository.save(order);
    }

    public Order queryOrder(Long orderId) {
        return orderRepository.findOrderByOrderId(orderId);
    }
}

UserService.java

@Slf4j
@Service
public class UserService {

    @Autowired
    private UserRepository userRepository;

    public void saveUser(User user) {
        userRepository.save(user);
    }

    public User queryUser(Long id) {
        return userRepository.findUserById(id);
    }
}

5.4、测试类

OrderTests.java

@Slf4j
@RunWith(SpringJUnit4ClassRunner.class)
@SpringBootTest
public class OrderTests {

    @Autowired
    private OrderService orderService;

    @Test
    public void saveOrder() {
        for (int i = 0; i < 10; i++) {
            Order order = new Order();
            order.setUserId(1000);
            // 随机生成50到100的金额
            int price = (int) Math.round(Math.random() * (10000 - 5000) + 5000);
            order.setPrice(price);
            order.setOrderStatus(2);
            order.setOrderTime(LocalDateTime.now());
            order.setTitle("");
            orderService.saveOrder(order);
        }
    }

    @Test
    public void queryOrder() {
        Long orderId = 845685274628734976L;
        Order order = orderService.queryOrder(orderId);
        log.info("查询的结果：{}", order);
    }

UserTests.java

@Slf4j
@RunWith(SpringJUnit4ClassRunner.class)
@SpringBootTest
public class UserTests {

    @Autowired
    private UserService userService;

    @Test
    public void saveUser() {
        for (int i = 0; i < 8; i++) {
            User user = new User();
            // 随机生成50到100的金额
            int age = (int) Math.round(Math.random() * (35 - 15) + 15);
            user.setUserName("user-" + age);
            user.setAge(age);
            userService.saveUser(user);
        }
    }

    @Test
    public void queryUser() {
        Long orderId = 845687246882754561L;
        User user = userService.queryUser(orderId);
        log.info("查询的结果：{}", user);
    }

}

5.4.1、保存订单数据

运行结果：

15:31:21 465 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 465 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_2 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:20.964, 7687, , 1000, 845685274007977984]
15:31:21 521 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 521 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_1 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.519, 5283, , 1000, 845685274528071681]
15:31:21 544 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 545 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_2 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.543, 7447, , 1000, 845685274628734976]
15:31:21 568 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 569 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_1 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.567, 8478, , 1000, 845685274729398273]
15:31:21 589 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 589 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_2 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.588, 9612, , 1000, 845685274813284352]
15:31:21 610 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 611 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_1 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.609, 5153, , 1000, 845685274905559041]
15:31:21 637 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 638 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_2 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.636, 6311, , 1000, 845685275018805248]
15:31:21 692 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 693 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_1 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.691, 8013, , 1000, 845685275249491969]
15:31:21 715 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 716 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_2 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.714, 8992, , 1000, 845685275345960960]
15:31:21 737 INFO [main]:Logic SQL: insert into tb_order (order_status, order_time, price, title, user_id) values (?, ?, ?, ?, ?)
15:31:21 737 INFO [main]:Actual SQL: ds1 ::: insert into tb_order_1 (order_status, order_time, price, title, user_id, order_id) values (?, ?, ?, ?, ?, ?) ::: [2, 2023-03-23 15:31:21.736, 5229, , 1000, 845685275438235649]

效果图：

5.4.2、查询订单数据

  从上面的结果我们可以看到order_id为 845685274628734976 的记录在 sharding_3 库的 tb_order_2 表，实际查询通过 ds1 去查询的请看下面的 Actual SQL

15:33:38 719 INFO [main]:Logic SQL: select order0_.order_id as order_id1_0_, order0_.order_status as order_st2_0_, order0_.order_time as order_ti3_0_, order0_.price as price4_0_, order0_.title as title5_0_, order0_.user_id as user_id6_0_ from tb_order order0_ where order0_.order_id=?
15:33:38 720 INFO [main]:Actual SQL: ds1 ::: select order0_.order_id as order_id1_0_, order0_.order_status as order_st2_0_, order0_.order_time as order_ti3_0_, order0_.price as price4_0_, order0_.title as title5_0_, order0_.user_id as user_id6_0_ from tb_order_2 order0_ where order0_.order_id=? ::: [845685274628734976]
15:33:38 772 INFO [main]:查询的结果：Order(orderId=845685274628734976, userId=1000, price=7447, orderStatus=2, title=, orderTime=2023-03-23T15:31:22)

5.4.3、保存用户数据

运行结果：

15:39:11 492 INFO [main]:Logic SQL: insert into tb_user (age, user_name) values (?, ?)
15:39:11 492 INFO [main]:Actual SQL: ds2 ::: insert into tb_user_2 (age, user_name, id) values (?, ?, ?) ::: [23, user-23, 845687245477662720]
15:39:11 574 INFO [main]:Logic SQL: insert into tb_user (age, user_name) values (?, ?)
15:39:11 574 INFO [main]:Actual SQL: ds2 ::: insert into tb_user_1 (age, user_name, id) values (?, ?, ?) ::: [21, user-21, 845687246077448193]
15:39:11 603 INFO [main]:Logic SQL: insert into tb_user (age, user_name) values (?, ?)
15:39:11 603 INFO [main]:Actual SQL: ds2 ::: insert into tb_user_2 (age, user_name, id) values (?, ?, ?) ::: [16, user-16, 845687246194888704]
15:39:11 630 INFO [main]:Logic SQL: insert into tb_user (age, user_name) values (?, ?)
15:39:11 631 INFO [main]:Actual SQL: ds2 ::: insert into tb_user_1 (age, user_name, id) values (?, ?, ?) ::: [22, user-22, 845687246312329217]
15:39:11 662 INFO [main]:Logic SQL: insert into tb_user (age, user_name) values (?, ?)
15:39:11 662 INFO [main]:Actual SQL: ds2 ::: insert into tb_user_2 (age, user_name, id) values (?, ?, ?) ::: [32, user-32, 845687246446546944]
15:39:11 695 INFO [main]:Logic SQL: insert into tb_user (age, user_name) values (?, ?)
15:39:11 695 INFO [main]:Actual SQL: ds2 ::: insert into tb_user_1 (age, user_name, id) values (?, ?, ?) ::: [20, user-20, 845687246580764673]
15:39:11 731 INFO [main]:Logic SQL: insert into tb_user (age, user_name) values (?, ?)
15:39:11 731 INFO [main]:Actual SQL: ds2 ::: insert into tb_user_2 (age, user_name, id) values (?, ?, ?) ::: [16, user-16, 845687246731759616]
15:39:11 766 INFO [main]:Logic SQL: insert into tb_user (age, user_name) values (?, ?)
15:39:11 766 INFO [main]:Actual SQL: ds2 ::: insert into tb_user_1 (age, user_name, id) values (?, ?, ?) ::: [17, user-17, 845687246882754561]

效果图：

5.4.4、查询用户数据

运行结果：

15:43:18 497 INFO [main]:Logic SQL: select user0_.id as id1_1_, user0_.age as age2_1_, user0_.create_time as create_t3_1_, user0_.update_time as update_t4_1_, user0_.user_name as user_nam5_1_ from tb_user user0_ where user0_.id=?
15:43:18 498 INFO [main]:Actual SQL: ds2 ::: select user0_.id as id1_1_, user0_.age as age2_1_, user0_.create_time as create_t3_1_, user0_.update_time as update_t4_1_, user0_.user_name as user_nam5_1_ from tb_user_1 user0_ where user0_.id=? ::: [845687246882754561]
15:43:18 577 INFO [main]:查询的结果：User(id=845687246882754561, userName=user-17, age=17, createTime=2023-03-23 07:39:11.0, updateTime=2023-03-23 07:39:11.0)

效果图：

  从上面的结果我们可以看到user_id为 845687246882754561 的记录在 sharding_4 库的 tb_user_1 表，实际查询通过 ds2 去查询的，请看下面的 Actual SQL