Springboot2.x +JPA 集成 Apache ShardingSphere 分表+读写分离

分库分表背景:
数据库性能瓶颈：主要分为按照业务来划分或者按照数据量来划分。
拆分方式：
水平拆分(每个表的结构都一样)：订单表数据量大，我们可以水平拆分，分成order表1、order表2、order表3 。。。
垂直拆分：一个多字段的表拆分成多个表
例如：order订单表和oderItem订单详情表
一个订单会购买多件商品，因此，订单order表中会只有一条数据，orderItem订单项表会对应这个订单购买的多件商品。

文章目录

- - - - 一、基础准备
      - 1. 技术选型
        2. 搭建mysql主从复制服务器
        1. 引入 Maven 依赖
        2. 规则配置
        3. 实体
        4. 接口
        5. 表结构
        6. 测试类
        7. 完整pom

一、基础准备

1. 技术选型

组件/框架	版本
spring-boot	2.4.3
jpa	2.4.3
shardingsphere	5.0.0-alpha
mysql	5.7.3
hikari	3.4.5

2. 搭建mysql主从复制服务器

基于Docker的Mysql主从复制搭建_mysql5.7.x

1. 引入 Maven 依赖

      <dependency><groupId>org.apache.shardingsphere</groupId><artifactId>shardingsphere-jdbc-core-spring-boot-starter</artifactId><version>5.0.0-alpha</version></dependency>

2. 规则配置

spring.shardingsphere.datasource.names=primary-ds,replica-ds-0spring.shardingsphere.datasource.common.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.common.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.common.username=root
spring.shardingsphere.datasource.common.password=123456spring.shardingsphere.datasource.primary-ds.jdbc-url=jdbc:mysql://192.168.43.202:3339/ds0?serverTimezone=UTC&useSSL=false
spring.shardingsphere.datasource.replica-ds-0.jdbc-url=jdbc:mysql://192.168.43.202:3340/ds0?serverTimezone=UTC&useSSL=falsespring.shardingsphere.rules.replica-query.data-sources.ds0.primary-data-source-name=primary-ds
spring.shardingsphere.rules.replica-query.data-sources.ds0.replica-data-source-names=replica-ds-0
spring.shardingsphere.rules.replica-query.data-sources.ds0.load-balancer-name=round-robin# 负载均衡算法配置
spring.shardingsphere.rules.replica-query.load-balancers.round-robin.type=ROUND_ROBIN
spring.shardingsphere.rules.replica-query.load-balancers.round-robin.props.default=0spring.shardingsphere.props.sql-show=true# 配置 t_order 表规则
spring.shardingsphere.rules.sharding.tables.t_order.actual-data-nodes=ds0.t_order_$->{0..1}# 配置分表策略
spring.shardingsphere.rules.sharding.tables.t_order.table-strategy.standard.sharding-column=order_id
spring.shardingsphere.rules.sharding.tables.t_order.table-strategy.standard.sharding-algorithm-name=table-inline# 配置 分片算法
spring.shardingsphere.rules.sharding.sharding-algorithms.table-inline.type=INLINE
spring.shardingsphere.rules.sharding.sharding-algorithms.table-inline.props.algorithm-expression=t_order_$->{order_id % 2}# 分布式序列策略配置
spring.shardingsphere.rules.sharding.tables.t_order.key-generate-strategy.column=order_id
spring.shardingsphere.rules.sharding.tables.t_order.key-generate-strategy.key-generator-name=snowflake# 分布式序列算法配置
spring.shardingsphere.rules.sharding.key-generators.snowflake.type=SNOWFLAKE
spring.shardingsphere.rules.sharding.key-generators.snowflake.props.worker-id=123

在这里插入图片描述

3. 实体

package com.gblfy.distributedsharding.entity;import lombok.Data;import javax.persistence.*;@Data
@Entity
@Table(name = "t_order")
public class OrderEntity {@Id@GeneratedValue(strategy = GenerationType.IDENTITY)private Long orderId;private Integer userId;
}

4. 接口

package com.gblfy.distributedsharding.mapper;import com.gblfy.distributedsharding.entity.OrderEntity;
import org.springframework.data.jpa.repository.JpaRepository;import java.util.List;public interface OrderMapper extends JpaRepository<OrderEntity, Long> {OrderEntity findByOrderId(Long orderId);List<OrderEntity> findByUserId(Integer userId);
}

5. 表结构

在master主库执行

CREATE DATABASE ds0;
use ds0;
CREATE TABLE `t_order_0` (`order_id` bigint(20) unsigned NOT NULL,`user_id` int(11) DEFAULT NULL,PRIMARY KEY (`order_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;CREATE TABLE `t_order_1` (`order_id` bigint(20) unsigned NOT NULL,`user_id` int(11) DEFAULT NULL,PRIMARY KEY (`order_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

6. 测试类

package com.gblfy.distributedsharding;import com.gblfy.distributedsharding.entity.OrderEntity;
import com.gblfy.distributedsharding.mapper.OrderMapper;
import org.apache.shardingsphere.infra.hint.HintManager;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;import java.util.Random;@SpringBootTest
class DistributedShardingApplicationTests {@Autowiredprivate OrderMapper orderMapper;@Testvoid insert() {for (int i = 0; i <20 ; i++) {OrderEntity entity = new OrderEntity();entity.setUserId(new Random().nextInt(999));orderMapper.save(entity);}}@Testvoid findByOrderId() {//在主库负责增删改查 从库负责查询 场景中 ，存在刚把数据写入主库中，// 为来得急同步从库，因此会导致从库没有查询的数据，但实际，数据在主库是存在的，// 从库负责查询只是为了环节主库的数据库查询的压力，因此，在特殊场景，需要从主库// 查询数据，可以通过配置五主库中查询数据HintManager.getInstance().setPrimaryRouteOnly();orderMapper.findByOrderId(570271967295811584L);}@Testvoid findByUserId() {orderMapper.findByUserId(556);}@Testvoid updateByOrderId() {OrderEntity byOrderId = orderMapper.findByOrderId(570279923689172992L);byOrderId.setUserId(1000);orderMapper.save(byOrderId);}
}

7. 完整pom

<dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-jpa</artifactId></dependency><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><scope>runtime</scope></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><optional>true</optional></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId><scope>test</scope></dependency><dependency><groupId>org.apache.shardingsphere</groupId><artifactId>shardingsphere-jdbc-core-spring-boot-starter</artifactId><version>5.0.0-alpha</version></dependency>