使用Sharding-JDBC对数据进行分片处理详解

目录

• 前言
• 一、加入依赖
• 二、修改application.yml配置文件
• 三、数据源定义
• 四、数据源分配算法实现
• 五、数据表分配算法
• 六、数据源配置
• 七、开始测试
• 定义一个实体
• 定义实体DAO
• 测试类，插入1000条user数据
• 效果：数据被分片存储到0~9的数据表中

前言

Sharding-JDBC是ShardingSphere的第一个产品，也是ShardingSphere的前身。

它定位为轻量级Java框架，在Java的JDBC层提供的额外服务。它使用客户端直连数据库，以jar包形式提供服务，无需额外部署和依赖，可理解为增强版的JDBC驱动，完全兼容JDBC和各种ORM框架。

• 适用于任何基于Java的ORM框架，如：JPA, Hibernate, Mybatis, Spring JDBC Template或直接使用JDBC。
• 基于任何第三方的数据库连接池，如：DBCP, C3P0, BoneCP, Druid, HikariCP等支持任意实现JDBC规范的数据库。
• 目前支持MySQL，Oracle，SQLServer和PostgreSQL。

Sharding-JDBC的使用需要我们对项目进行一些调整：结构如下

ShardingSphere文档地址

这里使用的是springBoot项目改造

一、加入依赖

    com.alibaba
    druid
    1.1.9



    com.dangdang
    sharding-jdbc-core
    1.5.4



    org.kcsm.common
    kcsm-idgenerator
    3.0.1

二、修改application.yml配置文件

#启动接口
server:
  port: 30009
spring:
  jpa:
    database: mysql
    show-sql: true
    hibernate:
#      修改不自动更新表
      ddl-auto: none
#数据源0定义，这里只是用了一个数据源，各位客官可以根据自己的需求定义多个数据源
database0:
  databaseName: database0
  url: jdbc:mysql://kcsm-pre.mysql.rds.aliyuncs.com:3306/dstest?characterEncoding=utf8&useUnicode=true&useSSL=false&serverTimezone=Hongkong
  username: root
  password: kcsm@111
  driverClassName: com.mysql.jdbc.Driver

三、数据源定义

package com.lzx.code.codedemo.config;
import com.alibaba.druid.pool.DruidDataSource;
import lombok.Data;
import org.springframework.boot.context.properties.ConfigurationProperties;
import org.springframework.stereotype.Component;
import javax.sql.DataSource;
/**
 * 描述:数据源0定义
 *
 * @Auther: lzx
 * @Date: 2019/9/9 15:19
 */
@Data
@ConfigurationProperties(prefix = "database0")
@Component
public class Database0Config {
    private String url;
    private String username;
    private String password;
    private String driverClassName;
    private String databaseName;
    public DataSource createDataSource() {
        DruidDataSource result = new DruidDataSource();
        result.setDriverClassName(getDriverClassName());
        result.setUrl(getUrl());
        result.setUsername(getUsername());
        result.setPassword(getPassword());
        return result;
    }
}

四、数据源分配算法实现

package com.lzx.code.codedemo.config;
import com.dangdang.ddframe.rdb.sharding.api.ShardingValue;
import com.dangdang.ddframe.rdb.sharding.api.strategy.database.SingleKeyDatabaseShardingAlgorithm;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;
/**
 * 描述:数据源分配算法
 *
 * 这里我们只用了一个数据源，所以所有的都只返回了数据源0
 *
 * @Auther: lzx
 * @Date: 2019/9/9 15:27
 */
@Component
public class DatabaseShardingAlgorithm implements SingleKeyDatabaseShardingAlgorithm {
    @Autowired
    private Database0Config database0Config;
    /**
     *  = 条件时候返回的数据源
     * @param collection
     * @param shardingValue
     * @return
     */
    @Override
    public String doEqualSharding(Collection collection, ShardingValue shardingValue) {
        return database0Config.getDatabaseName();
    }
    /**
     *  IN 条件返回的数据源
     * @param collection
     * @param shardingValue
     * @return
     */
    @Override
    public Collection doInSharding(Collection collection, ShardingValue shardingValue) {
        List result = new ArrayList();
        result.add(database0Config.getDatabaseName());
        return result;
    }
    /**
     * BETWEEN 条件放回的数据源
     * @param collection
     * @param shardingValue
     * @return
     */
    @Override
    public Collection doBetweenSharding(Collection collection, ShardingValue shardingValue) {
        List result = new ArrayList();
        result.add(database0Config.getDatabaseName());
        return result;
    }
}

五、数据表分配算法

package com.lzx.code.codedemo.config;
import com.dangdang.ddframe.rdb.sharding.api.ShardingValue;
import com.dangdang.ddframe.rdb.sharding.api.strategy.table.SingleKeyTableShardingAlgorithm;
import com.google.common.collect.Range;
import org.springframework.stereotype.Component;
import java.util.Collection;
import java.util.LinkedHashSet;
/**
 * 描述: 数据表分配算法的实现
 *
 * @Auther: lzx
 * @Date: 2019/9/9 16:19
 */
@Component
public class TableShardingAlgorithm implements SingleKeyTableShardingAlgorithm {
    /**
     * = 条件时候返回的数据源
     * @param collection
     * @param shardingValue
     * @return
     */
    @Override
    public String doEqualSharding(Collection collection, ShardingValue shardingValue) {
        for (String eaach:collection) {
            Long value = shardingValue.getValue();
            value = value >> 22;
            if(eaach.endsWith(value%10+"")){
                return eaach;
            }
        }
        throw new IllegalArgumentException();
    }
    /**
     * IN 条件返回的数据源
     * @param tableNames
     * @param shardingValue
     * @return
     */
    @Override
    public Collection doInSharding(Collection tableNames, ShardingValue shardingValue) {
        Collection result = new LinkedHashSet<>(tableNames.size());
        for (Long value : shardingValue.getValues()) {
            for (String tableName : tableNames) {
                value = value >> 22;
                if (tableName.endsWith(value % 10 + "")) {
                    result.add(tableName);
                }
            }
        }
        return result;
    }
    /**
     * BETWEEN 条件放回的数据源
     * @param tableNames
     * @param shardingValue
     * @return
     */
    @Override
    public Collection doBetweenSharding(Collection tableNames, ShardingValue shardingValue) {
        Collection result = new LinkedHashSet<>(tableNames.size());
        Range range = shardingValue.getValueRange();
        for (Long i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
            for (String each : tableNames) {
                Long value = i >> 22;
                if (each.endsWith(i % 10 + "")) {
                    result.add(each);
                }
            }
        }
        return result;
    }
}

六、数据源配置

package com.lzx.code.codedemo.config;
import com.dangdang.ddframe.rdb.sharding.api.ShardingDataSourceFactory;
import com.dangdang.ddframe.rdb.sharding.api.rule.DataSourceRule;
import com.dangdang.ddframe.rdb.sharding.api.rule.ShardingRule;
import com.dangdang.ddframe.rdb.sharding.api.rule.TableRule;
import com.dangdang.ddframe.rdb.sharding.api.strategy.database.DatabaseShardingStrategy;
import com.dangdang.ddframe.rdb.sharding.api.strategy.table.TableShardingStrategy;
import com.dangdang.ddframe.rdb.sharding.keygen.DefaultKeyGenerator;
import com.dangdang.ddframe.rdb.sharding.keygen.KeyGenerator;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import javax.sql.DataSource;
import java.sql.SQLException;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
/**
 * 描述:数据源配置
 *
 * @Auther: lzx
 * @Date: 2019/9/9 15:21
 */
@Configuration
public class DataSourceConfig {
    @Autowired
    private Database0Config database0Config;
    @Autowired
    private DatabaseShardingAlgorithm databaseShardingAlgorithm;
    @Autowired
    private TableShardingAlgorithm tableShardingAlgorithm;
    @Bean
    public DataSource getDataSource() throws SQLException {
        return buildDataSource();
    }
    private DataSource buildDataSource() throws SQLException {
        //分库设置
        Map dataSourceMap = new HashMap<>(2);
        //添加两个数据库database0和database1
        dataSourceMap.put(database0Config.getDatabaseName(), database0Config.createDataSource());
        //设置默认数据库
        DataSourceRule dataSourceRule = new DataSourceRule(dataSourceMap, database0Config.getDatabaseName());
        //分表设置，大致思想就是将查询虚拟表Goods根据一定规则映射到真实表中去
        TableRule orderTableRule = TableRule.builder("user")
                .actualTables(Arrays.asList("user_0", "user_1", "user_2", "user_3", "user_4", "user_5", "user_6", "user_7", "user_8", "user_9"))
                .dataSourceRule(dataSourceRule)
                .build();
        //分库分表策略
        ShardingRule shardingRule = ShardingRule.builder()
                .dataSourceRule(dataSourceRule)
                .tableRules(Arrays.asList(orderTableRule))
                .databaseShardingStrategy(new DatabaseShardingStrategy("ID", databaseShardingAlgorithm))
                .tableShardingStrategy(new TableShardingStrategy("ID", tableShardingAlgorithm)).build();
        DataSource dataSource = ShardingDataSourceFactory.createDataSource(shardingRule);
        return dataSource;
    }
    @Bean
    public KeyGenerator keyGenerator() {
        return new DefaultKeyGenerator();
    }
}

七、开始测试

定义一个实体

package com.lzx.code.codedemo.entity;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;
import com.fasterxml.jackson.databind.ser.std.ToStringSerializer;
import lombok.*;
import org.hibernate.annotations.GenericGenerator;
import javax.persistence.*;
/**
 * 描述: 用户
 *
 * @Auther: lzx
 * @Date: 2019/7/11 15:39
 */
@Entity(name = "USER")
@Getter
@Setter
@ToString
@JsonIgnoreProperties(ignoreUnknown = true)
@AllArgsConstructor
@NoArgsConstructor
public class User {
    /**
     * 主键
     */
    @Id
    @GeneratedValue(generator = "idUserConfig")
    @GenericGenerator(name ="idUserConfig" ,strategy="org.kcsm.common.ids.SerialIdGeneratorSnowflakeId")
    @Column(name = "ID", unique = true,nullable=false)
    @JsonSerialize(using = ToStringSerializer.class)
    private Long id;
    /**
     * 用户名
     */
    @Column(name = "USER_NAME",length = 100)
    private String userName;
    /**
     * 密码
     */
    @Column(name = "PASSWORD",length = 100)
    private String password;
}

定义实体DAO

package com.lzx.code.codedemo.dao;
import com.lzx.code.codedemo.entity.User;
import org.springframework.data.jpa.repository.JpaRepository;
import org.springframework.data.jpa.repository.JpaSpecificationExecutor;
import org.springframework.data.rest.core.annotation.RepositoryRestResource;
/**
 * 描述: 用户dao接口
 *
 * @Auther: lzx
 * @Date: 2019/7/11 15:52
 */
@RepositoryRestResource(path = "user")
public interface UserDao extends JpaRepository,JpaSpecificationExecutor {
}

测试类，插入1000条user数据

package com.lzx.code.codedemo;
import com.lzx.code.codedemo.dao.RolesDao;
import com.lzx.code.codedemo.dao.UserDao;
import com.lzx.code.codedemo.entity.Roles;
import com.lzx.code.codedemo.entity.User;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.test.context.junit4.SpringRunner;
@RunWith(SpringRunner.class)
@SpringBootTest
public class CodeDemoApplicationTests {
    @Autowired
    private UserDao userDao;
    @Autowired
    private RolesDao rolesDao;
    @Test
    public void contextLoads() {
        User user = null;
        Roles roles = null;
        for(int i=0;i<1000;i++){
            user = new User(
                    null,
                    "lzx"+i,
                    "123456"
            );
            roles = new Roles(
                    null,
                    "角色"+i
            );
            rolesDao.save(roles);
            userDao.save(user);
            try {
                Thread.sleep(100);
            } catch (InterruptedException e) {
                e.printStackTrace();
            }
        }
    }
}

效果：数据被分片存储到0~9的数据表中

 

以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。