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 */@Configurationpublic 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  edHashMap<>() ;        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.setData ShardingStrategyConfig(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.setData ShardingStrategyConfig(new StandardShardingStrategyConfiguration("phone", new DataSourceAlg()));        result.setTableShardingStrategyConfig(new StandardShardingStrategyConfiguration("phone", new TableTwoAlg()));        return result;    }}

• 测试代码执行流程

@RestControllerpublic 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地址：知了一笑https://github.com/cicadasmile/middle-ware-parent码云地址：知了一笑https://gitee.com/cicadasmile/middle-ware-parent