SpringBoot 2.0 整合sharding-jdbc中间件，实现数据分库分表

一、水平分割

1、水平分库
1)、概念：
以字段为依据，按照一定策略，将一个库中的数据拆分到多个库中。
2)、结果
每个库的结构都一样；数据都不一样；
所有库的并集是全量数据；
2、水平分表
1)、概念
以字段为依据，按照一定策略，将一个表中的数据拆分到多个表中。
2)、结果
每个表的结构都一样；数据都不一样；
所有表的并集是全量数据；

二、shard-jdbc 中间件

1、架构图

2、特点
1)、sharding-jdbc直接封装jdbc api，旧代码迁移成本几乎为零。
2)、适用于任何基于java的orm框架，如hibernate、mybatis等 。
3)、可基于任何第三方的数据库连接池，如dbcp、c3p0、 bonecp、druid等。
4)、以jar包形式提供服务，无proxy代理层，无需额外部署，无其他依赖。
5)、分片策略灵活，可支持等号、between、in等多维度分片，也可支持多分片键。
6)、sql解析功能完善，支持聚合、分组、排序、limit、or等查询。

三、项目演示

1、项目结构

springboot     2.0 版本
druid          1.1.13 版本
sharding-jdbc  3.1 版本

2、数据库配置

一台基础库映射（shard_one）
两台库做分库分表（shard_two，shard_three）。
表使用：table_one，table_two

3、核心代码块

数据源配置文件

spring:
  datasource:
    # 数据源：shard_one
    dataone:
      type: com.alibaba.druid.pool.druiddatasource
      druid:
        driverclassname: com.mysql.jdbc.driver
        url: jdbc:mysql://localhost:3306/shard_one?useunicode=true&characterencoding=utf8&zerodatetimebehavior=converttonull&usessl=false
        username: root
        password: 123
        initial-size: 10
        max-active: 100
        min-idle: 10
        max-wait: 60000
        pool-prepared-statements: true
        max-pool-prepared-statement-per-connection-size: 20
        time-between-eviction-runs-millis: 60000
        min-evictable-idle-time-millis: 300000
        max-evictable-idle-time-millis: 60000
        validation-query: select 1 from dual
        # validation-query-timeout: 5000
        test-on-borrow: false
        test-on-return: false
        test-while-idle: true
        connectionproperties: druid.stat.mergesql=true;druid.stat.slowsqlmillis=5000
    # 数据源：shard_two
    datatwo:
      type: com.alibaba.druid.pool.druiddatasource
      druid:
        driverclassname: com.mysql.jdbc.driver
        url: jdbc:mysql://localhost:3306/shard_two?useunicode=true&characterencoding=utf8&zerodatetimebehavior=converttonull&usessl=false
        username: root
        password: 123
        initial-size: 10
        max-active: 100
        min-idle: 10
        max-wait: 60000
        pool-prepared-statements: true
        max-pool-prepared-statement-per-connection-size: 20
        time-between-eviction-runs-millis: 60000
        min-evictable-idle-time-millis: 300000
        max-evictable-idle-time-millis: 60000
        validation-query: select 1 from dual
        # validation-query-timeout: 5000
        test-on-borrow: false
        test-on-return: false
        test-while-idle: true
        connectionproperties: druid.stat.mergesql=true;druid.stat.slowsqlmillis=5000
    # 数据源：shard_three
    datathree:
      type: com.alibaba.druid.pool.druiddatasource
      druid:
        driverclassname: com.mysql.jdbc.driver
        url: jdbc:mysql://localhost:3306/shard_three?useunicode=true&characterencoding=utf8&zerodatetimebehavior=converttonull&usessl=false
        username: root
        password: 123
        initial-size: 10
        max-active: 100
        min-idle: 10
        max-wait: 60000
        pool-prepared-statements: true
        max-pool-prepared-statement-per-connection-size: 20
        time-between-eviction-runs-millis: 60000
        min-evictable-idle-time-millis: 300000
        max-evictable-idle-time-millis: 60000
        validation-query: select 1 from dual
        # validation-query-timeout: 5000
        test-on-borrow: false
        test-on-return: false
        test-while-idle: true
        connectionproperties: druid.stat.mergesql=true;druid.stat.slowsqlmillis=5000

数据库分库策略

/**
 * 数据库映射计算
 */
public class datasourcealg implements preciseshardingalgorithm<string> {

    private static logger log = loggerfactory.getlogger(datasourcealg.class);
    @override
    public string dosharding(collection<string> names, preciseshardingvalue<string> value) {
        log.debug("分库算法参数 {},{}",names,value);
        int hash = hashutil.rshash(string.valueof(value.getvalue()));
        return "ds_" + ((hash % 2) + 2) ;
    }
}

数据表1分表策略

/**
 * 分表算法
 */
public class tableonealg implements preciseshardingalgorithm<string> {
    private static logger log = loggerfactory.getlogger(tableonealg.class);
    /**
     * 该表每个库分5张表
     */
    @override
    public string dosharding(collection<string> names, preciseshardingvalue<string> value) {
        log.debug("分表算法参数 {},{}",names,value);
        int hash = hashutil.rshash(string.valueof(value.getvalue()));
        return "table_one_" + (hash % 5+1);
    }
}

数据表2分表策略

/**
 * 分表算法
 */
public class tabletwoalg implements preciseshardingalgorithm<string> {
    private static logger log = loggerfactory.getlogger(tabletwoalg.class);
    /**
     * 该表每个库分5张表
     */
    @override
    public string dosharding(collection<string> names, preciseshardingvalue<string> value) {
        log.debug("分表算法参数 {},{}",names,value);
        int hash = hashutil.rshash(string.valueof(value.getvalue()));
        return "table_two_" + (hash % 5+1);
    }
}

数据源集成配置

/**
 * 数据库分库分表配置
 */
@configuration
public class shardjdbcconfig {
    // 省略了 druid 配置，源码中有
    /**
     * shard-jdbc 分库配置
     */
    @bean
    public datasource datasource (@autowired druiddatasource dataonesource,
                                  @autowired druiddatasource datatwosource,
                                  @autowired druiddatasource datathreesource) throws exception {
        shardingruleconfiguration shardjdbcconfig = new shardingruleconfiguration();
        shardjdbcconfig.gettableruleconfigs().add(gettablerule01());
        shardjdbcconfig.gettableruleconfigs().add(gettablerule02());
        shardjdbcconfig.setdefaultdatasourcename("ds_0");
        map<string,datasource> datamap = new linkedhashmap<>() ;
        datamap.put("ds_0",dataonesource) ;
        datamap.put("ds_2",datatwosource) ;
        datamap.put("ds_3",datathreesource) ;
        properties prop = new properties();
        return shardingdatasourcefactory.createdatasource(datamap, shardjdbcconfig, new hashmap<>(), prop);
    }

    /**
     * shard-jdbc 分表配置
     */
    private static tableruleconfiguration gettablerule01() {
        tableruleconfiguration result = new tableruleconfiguration();
        result.setlogictable("table_one");
        result.setactualdatanodes("ds_${2..3}.table_one_${1..5}");
        result.setdatabaseshardingstrategyconfig(new standardshardingstrategyconfiguration("phone", new datasourcealg()));
        result.settableshardingstrategyconfig(new standardshardingstrategyconfiguration("phone", new tableonealg()));
        return result;
    }
    private static tableruleconfiguration gettablerule02() {
        tableruleconfiguration result = new tableruleconfiguration();
        result.setlogictable("table_two");
        result.setactualdatanodes("ds_${2..3}.table_two_${1..5}");
        result.setdatabaseshardingstrategyconfig(new standardshardingstrategyconfiguration("phone", new datasourcealg()));
        result.settableshardingstrategyconfig(new standardshardingstrategyconfiguration("phone", new tabletwoalg()));
        return result;
    }
}

测试代码执行流程

@restcontroller
public class shardcontroller {
    @resource
    private shardservice shardservice ;
    /**
     * 1、建表流程
     */
    @requestmapping("/createtable")
    public string createtable (){
        shardservice.createtable();
        return "success" ;
    }
    /**
     * 2、生成表 table_one 数据
     */
    @requestmapping("/insertone")
    public string insertone (){
        shardservice.insertone();
        return "success" ;
    }
    /**
     * 3、生成表 table_two 数据
     */
    @requestmapping("/inserttwo")
    public string inserttwo (){
        shardservice.inserttwo();
        return "success" ;
    }
    /**
     * 4、查询表 table_one 数据
     */
    @requestmapping("/selectonebyphone/{phone}")
    public tableone selectonebyphone (@pathvariable("phone") string phone){
        return shardservice.selectonebyphone(phone);
    }
    /**
     * 5、查询表 table_one 数据
     */
    @requestmapping("/selecttwobyphone/{phone}")
    public tabletwo selecttwobyphone (@pathvariable("phone") string phone){
        return shardservice.selecttwobyphone(phone);
    }
}

四、项目源码

github：知了一笑
https://github.com/cicadasmile/middle-ware-parent