Sharding JDBC-分库分表

本文作者：梁开权，叩丁狼高级讲师。原创文章，转载请注明出处。

环境准备

pom.xml

<parent>
    <groupId>org.springframework.bootgroupId>
    <artifactId>spring-boot-starter-parentartifactId>
    <version>2.1.3.RELEASEversion>
parent>

<properties>
    <java.version>1.8java.version>
    <sharding.version>3.1.0sharding.version>
properties>

<dependencies>
    <dependency>
        <groupId>io.shardingspheregroupId>
        <artifactId>sharding-jdbc-coreartifactId>
        <version>${sharding.version}version>
    dependency>

    <dependency>
        <groupId>io.shardingspheregroupId>
        <artifactId>sharding-jdbc-spring-boot-starterartifactId>
        <version>${sharding.version}version>
    dependency>

    <dependency>
        <groupId>com.alibabagroupId>
        <artifactId>druidartifactId>
        <version>1.1.10version>
    dependency>

    <dependency>
        <groupId>org.mybatisgroupId>
        <artifactId>mybatisartifactId>
        <version>3.4.5version>
    dependency>

    <dependency>
        <groupId>org.mybatis.spring.bootgroupId>
        <artifactId>mybatis-spring-boot-starterartifactId>
        <version>1.3.1version>
    dependency>

    <dependency>
        <groupId>mysqlgroupId>
        <artifactId>mysql-connector-javaartifactId>
        <version>5.1.46version>
    dependency>

    <dependency>
        <groupId>org.springframework.bootgroupId>
        <artifactId>spring-boot-starterartifactId>
    dependency>

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

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

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

domain

// 建立domain
@Setter@Getter@ToString
@NoArgsConstructor
@AllArgsConstructor
public class Employee {
     
    private Long id;
    private String name;
}

配置类

@SpringBootApplication
@MapperScan("cn.wolfcode.sharding.mapper")
public class ShardingApplication {
      }

分库分表

案例模型

把数据分别存放在两台服务器的两个数据库中表，通过分片算法来决定当前的数据存放在哪个数据库的哪个表中，由于一个连接池只能连接一个特定的数据库，所以这里需要创建多个连接池对象

建表

-- 分别在2台服务器中建立数据库sharding，并且建表employee_0和employee_1
CREATE TABLE `employee_0` (
  `id` bigint(20) PRIMARY KEY AUTO_INCREMENT,
  `name` varchar(255) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
-- ###################################
CREATE TABLE `employee_1` (
  `id` bigint(20) PRIMARY KEY AUTO_INCREMENT,
  `name` varchar(255) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

application.properties

# 定义连接池
sharding.jdbc.datasource.names=db0,db1

# 格式sharding.jdbc.datasource.连接池名.xxx:设置4要素信息
sharding.jdbc.datasource.db0.type=com.alibaba.druid.pool.DruidDataSource
sharding.jdbc.datasource.db0.driver-class-name=com.mysql.jdbc.Driver
sharding.jdbc.datasource.db0.url=jdbc:mysql://db0Ip:port/sharing
sharding.jdbc.datasource.db0.username=xxx
sharding.jdbc.datasource.db0.password=xxx

sharding.jdbc.datasource.db1.type=com.alibaba.druid.pool.DruidDataSource
sharding.jdbc.datasource.db1.driver-class-name=com.mysql.jdbc.Driver
sharding.jdbc.datasource.db1.url=jdbc:mysql://db1Ip:port/sharing
sharding.jdbc.datasource.db1.username=xxx
sharding.jdbc.datasource.db1.password=xxx

# 设置分库规则
# sharding.jdbc.config.sharding.default-database-strategy.inline.sharding-column：分库列
# sharding.jdbc.config.sharding.default-database-strategy.inline.algorithm-expression：分库算法
sharding.jdbc.config.sharding.default-database-strategy.inline.sharding-column=id
sharding.jdbc.config.sharding.default-database-strategy.inline.algorithm-expression=db$->{id % 2}

# 绑定逻辑表
sharding.jdbc.config.sharding.binding-tables=employee

# 设置分表规则
# sharding.jdbc.config.sharding.tables.逻辑表.actual-data-nodes：逻辑表对应的真实表
# sharding.jdbc.config.sharding.tables.逻辑表.table-strategy.inline.sharding-column：分表列
# sharding.jdbc.config.sharding.tables.逻辑表.table-strategy.inline.algorithm-expression：分表算法
# sharding.jdbc.config.sharding.tables.逻辑表.key-generator-column-name：主键列
sharding.jdbc.config.sharding.tables.employee.actual-data-nodes=db$->{0..1}.employee_$->{0..1}
sharding.jdbc.config.sharding.tables.employee.table-strategy.inline.sharding-column=id
sharding.jdbc.config.sharding.tables.employee.table-strategy.inline.algorithm-expression=employee_$->{id % 2}
sharding.jdbc.config.sharding.tables.employee.key-generator-column-name=id

# 打印日志
sharding.jdbc.config.props.sql.show=true

mapper

/**
 * 这里写的employee表是上面所配置的逻辑表
 * 底层会根据分片规则，把我们写的逻辑表改写为数据库中的真实表
 */
@Mapper
public interface EmployeeMapper {
     
    @Select("select * from employee")
    List<Employee> selectAll();

    @Insert("insert into employee (name) values (#{name})")
    void inser(Employee entity);
}

测试

@RunWith(SpringRunner.class)
@SpringBootTest(classes=ShardingApplication.class)
public class ShardingApplicationTests {
     

	@Autowired
	private EmployeeMapper employeeMapper;

	@Test
	public void save() {
     
		for (int i = 0; i < 10; i++) {
     
			Employee employee = new Employee();
			employee.setName("xx"+i);
			employeeMapper.inser(employee);
		}
	}

	@Test
	public void list() {
     
		employeeMapper.selectAll().forEach(System.out::println);
	}
}

优缺点

• 拆分后单表数据量比较小,单表大数据被拆分,解决了单表大数据访问问题
• 分表以什么切分如果弄的不好,导致多次查询,而且有时候要跨库操作,甚至导致join无法使用,对排序分组等有性能影响
• 之前的原子操作被拆分成多个操作,事务处理变得复杂
• 多个DB维护成本增加