本文主要是介绍基于shard-jdbc中间件，实现数据分库分表，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
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
1）、数据源配置文件
spring:datasource:# 数据源：shard_onedataOne:type: com.alibaba.druid.pool.DruidDataSourcedruid:driverClassName: com.mysql.jdbc.Driverurl: jdbc:mysql://localhost:3306/shard_one?useUnicode=true&characterEncoding=UTF8&zeroDateTimeBehavior=convertToNull&useSSL=falseusername: rootpassword: 123initial-size: 10max-active: 100min-idle: 10max-wait: 60000pool-prepared-statements: truemax-pool-prepared-statement-per-connection-size: 20time-between-eviction-runs-millis: 60000min-evictable-idle-time-millis: 300000max-evictable-idle-time-millis: 60000validation-query: SELECT 1 FROM DUAL# validation-query-timeout: 5000test-on-borrow: falsetest-on-return: falsetest-while-idle: trueconnectionProperties: druid.stat.mergeSql=true;druid.stat.slowSqlMillis=5000# 数据源：shard_twodataTwo:type: com.alibaba.druid.pool.DruidDataSourcedruid:driverClassName: com.mysql.jdbc.Driverurl: jdbc:mysql://localhost:3306/shard_two?useUnicode=true&characterEncoding=UTF8&zeroDateTimeBehavior=convertToNull&useSSL=falseusername: rootpassword: 123initial-size: 10max-active: 100min-idle: 10max-wait: 60000pool-prepared-statements: truemax-pool-prepared-statement-per-connection-size: 20time-between-eviction-runs-millis: 60000min-evictable-idle-time-millis: 300000max-evictable-idle-time-millis: 60000validation-query: SELECT 1 FROM DUAL# validation-query-timeout: 5000test-on-borrow: falsetest-on-return: falsetest-while-idle: trueconnectionProperties: druid.stat.mergeSql=true;druid.stat.slowSqlMillis=5000# 数据源：shard_threedataThree:type: com.alibaba.druid.pool.DruidDataSourcedruid:driverClassName: com.mysql.jdbc.Driverurl: jdbc:mysql://localhost:3306/shard_three?useUnicode=true&characterEncoding=UTF8&zeroDateTimeBehavior=convertToNull&useSSL=falseusername: rootpassword: 123initial-size: 10max-active: 100min-idle: 10max-wait: 60000pool-prepared-statements: truemax-pool-prepared-statement-per-connection-size: 20time-between-eviction-runs-millis: 60000min-evictable-idle-time-millis: 300000max-evictable-idle-time-millis: 60000validation-query: SELECT 1 FROM DUAL# validation-query-timeout: 5000test-on-borrow: falsetest-on-return: falsetest-while-idle: trueconnectionProperties: druid.stat.mergeSql=true;druid.stat.slowSqlMillis=5000
2）、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);@Overridepublic 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) ;}
}
3）、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*/@Overridepublic 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);}
}
4）、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*/@Overridepublic 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);}
}
5）、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*/@Beanpublic 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;}
}
6）、测试代码执行流程
@RestController
public class ShardController {@Resourceprivate 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);}
}
本文分享自微信公众号 - 知了一笑（cicada_smile）
这篇关于基于shard-jdbc中间件，实现数据分库分表的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
