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 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); }}
四、项目源码
GitHub：知了一笑https://github.com/cicadasmile/middle-ware-parent
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