SpringBoot2如何整合Sharding-Jdbc中间件实现数据分库分表

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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 {  private static Logger LOG = LoggerFactory.getLogger(DataSourceAlg.class);  @Override  public String doSharding(Collection names, PreciseShardingValue 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 {  private static Logger LOG = LoggerFactory.getLogger(TableOneAlg.class);  /**   * 该表每个库分5张表   */  @Override  public String doSharding(Collection names, PreciseShardingValue 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 {  private static Logger LOG = LoggerFactory.getLogger(TableTwoAlg.class);  /**   * 该表每个库分5张表   */  @Override  public String doSharding(Collection names, PreciseShardingValue 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 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

  @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);  }}

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