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
<!-- 这里使用了druid连接池 -->
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>druid</artifactId>
<version>1.1.9</version>
</dependency>
<!-- sharding-jdbc 包 -->
<dependency>
<groupId>com.dangdang</groupId>
<artifactId>sharding-jdbc-core</artifactId>
<version>1.5.4</version>
</dependency>
<!-- 这里使用了雪花算法生成组建，这个算法的实现的自己写的代码，各位客关老爷可以修改为自己的id生成策略 -->
<dependency>
<groupId>org.kcsm.common</groupId>
<artifactId>kcsm-idgenerator</artifactId>
<version>3.0.1</version>
</dependency>
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;
@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;
@Component
public class DatabaseShardingAlgorithm implements SingleKeyDatabaseShardingAlgorithm {
@Autowired
private Database0Config database0Config;
@Override
public String doEqualSharding(Collection collection, ShardingValue shardingValue) {
return database0Config.getDatabaseName();
}
@Override
public Collection<String> doInSharding(Collection collection, ShardingValue shardingValue) {
List<String> result = new ArrayList<String>();
result.add(database0Config.getDatabaseName());
return result;
}
@Override
public Collection<String> doBetweenSharding(Collection collection, ShardingValue shardingValue) {
List<String> result = new ArrayList<String>();
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;
@Component
public class TableShardingAlgorithm implements SingleKeyTableShardingAlgorithm<Long> {
@Override
public String doEqualSharding(Collection<String> collection, ShardingValue<Long> shardingValue) {
for (String eaach:collection) {
Long value = shardingValue.getValue();
value = value >> 22;
if(eaach.endsWith(value%10+"")){
return eaach;
}
}
throw new IllegalArgumentException();
}
@Override
public Collection<String> doInSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
Collection<String> 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;
}
@Override
public Collection<String> doBetweenSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
Collection<String> result = new LinkedHashSet<>(tableNames.size());
Range<Long> 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;
@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<String, DataSource> 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.*;
@Entity(name = "USER")
@Getter
@Setter
@ToString
@JsonIgnoreProperties(ignoreUnknown = true)
@AllArgsConstructor
@NoArgsConstructor
public class User {
@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;
@Column(name = "USER_NAME",length = 100)
private String userName;
@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;
@RepositoryRestResource(path = "user")
public interface UserDao extends JpaRepository<User,Long>,JpaSpecificationExecutor<User> {
}
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编程网。
