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ShardingJdbc整合水平分表

整合 水平 分表
2023-09-11 14:16:49 时间

 创建数据库

DROP TABLE IF EXISTS `t_order_1`;
CREATE TABLE `t_order_1`(
`order_id` bigint(20) NOT NULL COMMENT'订单id',
`price` decimal(10,2) NOT NULL COMMENT'订单价格',
`user_id` bigint(20) NOT NULL COMMENT'下单用户id', 
`status`varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '订单状态',  PRIMARY KEY (`order_id`) USING BTREE
)ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;
DROP TABLE IF EXISTS `t_order_2`;
CREATE TABLE `t_order_2`(
`order_id` bigint(20) NOT NULL COMMENT '订单id',
`price` decimal(10,2) NOT NULL COMMENT '订单价格',
`user_id` bigint(20) NOT NULL COMMENT '下单用户id',
`status`varchar(50) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '订单状态',  PRIMARY KEY (`order_id`) USING BTREE
)ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;

 

 引入maven依赖 

<dependencies>

        <dependency>
            <groupId>org.mybatis.spring.boot</groupId>
            <artifactId>mybatis-spring-boot-starter</artifactId>
            <version>2.1.1</version>
        </dependency>


        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>druid-spring-boot-starter</artifactId>
            <version>1.1.16</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.47</version>
        </dependency>

        <dependency>
            <groupId>org.apache.shardingsphere</groupId>
            <artifactId>sharding-jdbc-spring-boot-starter</artifactId>
            <version>4.0.0-RC1</version>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
    </dependencies>

分片规则配置
分片规则配置是sharding-jdbc进行对分库分表操作的重要依据，配置内容包括：数据源、主键生成策略、分片策
略等。
在application.properties中配置 

server.port=56081

spring.application.name = sharding-jdbc-simple-demo

server.servlet.context-path = /sharding-jdbc-simple-demo
spring.http.encoding.enabled = true
spring.http.encoding.charset = UTF-8
spring.http.encoding.force = true

spring.main.allow-bean-definition-overriding = true

mybatis.configuration.map-underscore-to-camel-case = true

#sharding-jdbc分片规则配置
#数据源
spring.shardingsphere.datasource.names = m1

spring.shardingsphere.datasource.m1.type = com.alibaba.druid.pool.DruidDataSource
spring.shardingsphere.datasource.m1.driver-class-name = com.mysql.jdbc.Driver
spring.shardingsphere.datasource.m1.url = jdbc:mysql://localhost:3306/order_db?useUnicode=true
spring.shardingsphere.datasource.m1.username = root
spring.shardingsphere.datasource.m1.password = mysql

# 指定t_order表的数据分布情况，配置数据节点 m1.t_order_1,m1.t_order_2
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes = m1.t_order_$->{1..2}

# 指定t_order表的主键生成策略为SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE

# 指定t_order表的分片策略，分片策略包括分片键和分片算法
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column = order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression = t_order_$->{order_id % 2 + 1}

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

swagger.enable = true

logging.level.root = info
logging.level.org.springframework.web = info
logging.level.com.itheima.dbsharding  = debug
logging.level.druid.sql = debug

1.首先定义数据源m1，并对m1进行实际的参数配置。
2.指定t_order表的数据分布情况，他分布在m1.t_order_1，m1.t_order_2
3.指定t_order表的主键生成策略为SNOWFLAKE，SNOWFLAKE是一种分布式自增算法，保证id全局唯一
4.定义t_order分片策略，order_id为偶数的数据落在t_order_1，为奇数的落在t_order_2，分表策略的表达式为t_order_$->{order_id % 2 + 1} 

数据库操作

package com.topcheer.dbsharding.simple.dao;


import org.apache.ibatis.annotations.Insert;
import org.apache.ibatis.annotations.Mapper;
import org.apache.ibatis.annotations.Param;
import org.apache.ibatis.annotations.Select;
import org.springframework.stereotype.Component;

import java.math.BigDecimal;
import java.util.List;
import java.util.Map;

/**
 * Created by Administrator.
 */
@Mapper
@Component
public interface OrderDao {

    /**
     * 插入订单
     * @param price
     * @param userId
     * @param status
     * @return
     */
    @Insert("insert into t_order(price,user_id,status)values(#{price},#{userId},#{status})")
    int insertOrder(@Param("price") BigDecimal price, @Param("userId") Long userId, @Param("status") String status);

    /**
     * 根据id列表查询订单
     * @param orderIds
     * @return
     */
    @Select("<script>" +
            "select" +
            " * " +
            " from t_order t " +
            " where t.order_id in " +
            " <foreach collection='orderIds' open='(' separator=',' close=')' item='id'>" +
            " #{id} " +
            " </foreach>" +
            "</script>")
    List<Map> selectOrderbyIds(@Param("orderIds") List<Long> orderIds);
}

 

测试类

@RunWith(SpringRunner.class)
@SpringBootTest(classes = {ShardingJdbcSimpleBootstrap.class})
public class ShardingJdbcDemoApplicationTests {

    @Autowired(required = false)
    OrderDao orderDao;

    @Test
    public void testInsertOrder(){
        for(int i=1;i<20;i++){
            orderDao.insertOrder(new BigDecimal(i),1L,"SUCCESS");
        }
    }

    @Test
    public void testSelectOrderbyIds(){
        List<Long> ids = new ArrayList<>();
        ids.add(435435795839451136L);
        ids.add(435435794501468161L);

        List<Map> maps = orderDao.selectOrderbyIds(ids);
        System.out.println(maps);
    }

}

 

当执行插入的方法的时候, 会把原来的sql进行解析，然后根据分片的规则，进行插入不同的表

 

但执行查询的时候

 

 通过日志可以发现，根据传入order_id的奇偶不同，sharding-jdbc分别去不同的表检索数据，达到预期目标

流程分析
通过日志分析，Sharding-JDBC在拿到用户要执行的sql之后干了哪些事儿：
（1）解析sql，获取片键值，在本例中是order_id
（2）Sharding-JDBC通过规则配置 t_order_$->{order_id % 2 + 1}，知道了当order_id为偶数时，应该往
t_order_1表插数据，为奇数时，往t_order_2插数据。
（3）于是Sharding-JDBC根据order_id的值改写sql语句，改写后的SQL语句是真实所要执行的SQL语句。
（4）执行改写后的真实sql语句
（5）将所有真正执行sql的结果进行汇总合并，返回。

注意假如是配置类的形式进行配置的话，要排除

@Configuration
public class ShardingJdbcConfig {

    //配置分片规则
    // 定义数据源
    Map<String, DataSource> createDataSourceMap() {
        DruidDataSource dataSource1 = new DruidDataSource();
        dataSource1.setDriverClassName("com.mysql.jdbc.Driver");
        dataSource1.setUrl("jdbc:mysql://rm-bp1y5jh79h6b3eh9clo.mysql.rds.aliyuncs.com:3306/order_db?useUnicode=true");
        dataSource1.setUsername("root");
        dataSource1.setPassword("1qaz@WSX");
        Map<String, DataSource> result = new HashMap<>();
        result.put("m1", dataSource1);
        return result;
    }
    // 定义主键生成策略
    private static KeyGeneratorConfiguration getKeyGeneratorConfiguration() {
        KeyGeneratorConfiguration result = new KeyGeneratorConfiguration("SNOWFLAKE","order_id");
        return result;
    }

    // 定义t_order表的分片策略
    TableRuleConfiguration getOrderTableRuleConfiguration() {
        TableRuleConfiguration result = new TableRuleConfiguration("t_order","m1.t_order_$->{1..2}");
        result.setTableShardingStrategyConfig(new InlineShardingStrategyConfiguration("order_id", "t_order_$->{order_id % 2 + 1}"));
        result.setKeyGeneratorConfig(getKeyGeneratorConfiguration());

        return result;
    }
    // 定义sharding-Jdbc数据源
    @Bean
    DataSource getShardingDataSource() throws SQLException {
        ShardingRuleConfiguration shardingRuleConfig = new ShardingRuleConfiguration();
        shardingRuleConfig.getTableRuleConfigs().add(getOrderTableRuleConfiguration());
        //spring.shardingsphere.props.sql.show = true
        Properties properties = new Properties();
        properties.put("sql.show","true");
        return ShardingDataSourceFactory.createDataSource(createDataSourceMap(), shardingRuleConfig,properties);
    }

}

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