分庫分表背景:
數據庫性能瓶頸：主要分為按照業務來劃分或者按照數據量來劃分。
拆分方式：
水平拆分(每個表的結構都一樣)：訂單表數據量大，我們可以水平拆分 ，分成order表1、order表2、order表3 。。。
垂直拆分：一個多字段的表拆分成多個表
例如：order訂單表和oderItem訂單詳情表
一個訂單會購買多件商品，因此，訂單order表中會只有一條數據，orderItem訂單項表會對應這個訂單購買的多件商品。

文章目錄

• • • • • 一、基礎準備
        • • 1. 技術選型
          • 2. 搭建mysql主從復制服務器
          • 1. 引入 Maven 依賴
          • 2. 規則配置
          • 3. 實體
          • 4. 接口
          • 5. 表結構
          • 6. 測試類
          • 7. 完整pom

一、基礎準備

1. 技術選型

組件/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主從復制服務器

基于Docker的Mysql主從復制搭建_mysql5.7.x

1. 引入 Maven 依賴

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

2. 規則配置

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# 負載均衡算法配置 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 表規則 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

3. 實體

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. 表結構

在master主庫執行

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. 測試類

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() {//在主庫負責增刪改查 從庫負責查詢 場景中 ，存在剛把數據寫入主庫中，// 為來得急同步從庫，因此會導致從庫沒有查詢的數據，但實際，數據在主庫是存在的，// 從庫負責查詢只是為了環節主庫的數據庫查詢的壓力，因此，在特殊場景，需要從主庫// 查詢數據，可以通過配置五主庫中查詢數據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>

從庫查詢

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