SpringBoot 2.0 整合sharding-jdbc中介軟體，實現資料分庫分表
阿新 • • 發佈：2019-07-18
一、水平分割
1、水平分庫
1)、概念：
以欄位為依據，按照一定策略，將一個庫中的資料拆分到多個庫中。
2)、結果
每個庫的結構都一樣；資料都不一樣；
所有庫的並集是全量資料；
2、水平分表
1)、概念
以欄位為依據，按照一定策略，將一個表中的資料拆分到多個表中。
2)、結果
每個表的結構都一樣；資料都不一樣；
所有表的並集是全量資料；
二、Shard-jdbc 中介軟體
1、架構圖
2、特點
1)、Sharding-JDBC直接封裝JDBC API，舊程式碼遷移成本幾乎為零。 2)、適用於任何基於Java的ORM框架，如Hibernate、Mybatis等 。 3)、可基於任何第三方的資料庫連線池，如DBCP、C3P0、 BoneCP、Druid等。 4)、以jar包形式提供服務，無proxy代理層，無需額外部署，無其他依賴。 5)、分片策略靈活，可支援等號、between、in等多維度分片，也可支援多分片鍵。 6)、SQL解析功能完善，支援聚合、分組、排序、limit、or等查詢。
三、專案演示
1、專案結構
springboot 2.0 版本
druid 1.1.13 版本
sharding-jdbc 3.1 版本
2、資料庫配置
一臺基礎庫對映（shard_one）
兩臺庫做分庫分表（shard_two，shard_three）。
表使用：table_one，table_two
3、核心程式碼塊
資料來源配置檔案
spring: datasource: # 資料來源：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 # 資料來源：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 # 資料來源：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
資料庫分庫策略
/** * 資料庫對映計算 */ 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("分庫演算法引數 {},{}",names,value); int hash = HashUtil.rsHash(String.valueOf(value.getValue())); return "ds_" + ((hash % 2) + 2) ; } }
資料表1分表策略
/**
* 分表演算法
*/
public class TableOneAlg implements PreciseShardingAlgorithm<string> {
private static Logger LOG = LoggerFactory.getLogger(TableOneAlg.class);
/**
* 該表每個庫分5張表
*/
@Override
public String doSharding(Collection<string> names, PreciseShardingValue<string> value) {
LOG.debug("分表演算法引數 {},{}",names,value);
int hash = HashUtil.rsHash(String.valueOf(value.getValue()));
return "table_one_" + (hash % 5+1);
}
}
資料表2分表策略
/**
* 分表演算法
*/
public class TableTwoAlg implements PreciseShardingAlgorithm<string> {
private static Logger LOG = LoggerFactory.getLogger(TableTwoAlg.class);
/**
* 該表每個庫分5張表
*/
@Override
public String doSharding(Collection<string> names, PreciseShardingValue<string> value) {
LOG.debug("分表演算法引數 {},{}",names,value);
int hash = HashUtil.rsHash(String.valueOf(value.getValue()));
return "table_two_" + (hash % 5+1);
}
}
資料來源整合配置
/**
* 資料庫分庫分表配置
*/
@Configuration
public class ShardJdbcConfig {
// 省略了 druid 配置，原始碼中有
/**
* Shard-JDBC 分庫配置
*/
@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;
}
}
測試程式碼執行流程
@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 資料
*/
@RequestMapping("/insertOne")
public String insertOne (){
shardService.insertOne();
return "SUCCESS" ;
}
/**
* 3、生成表 table_two 資料
*/
@RequestMapping("/insertTwo")
public String insertTwo (){
shardService.insertTwo();
return "SUCCESS" ;
}
/**
* 4、查詢表 table_one 資料
*/
@RequestMapping("/selectOneByPhone/{phone}")
public TableOne selectOneByPhone (@PathVariable("phone") String phone){
return shardService.selectOneByPhone(phone);
}
/**
* 5、查詢表 table_one 資料
*/
@RequestMapping("/selectTwoByPhone/{phone}")
public TableTwo selectTwoByPhone (@PathVariable("phone") String phone){
return shardService.selectTwoByPhone(phone);
}
}
四、專案原始碼
GitHub地址：知了一笑
https://github.com/cicadasmile/middle-ware-parent
碼雲地址：知了一笑
https://gitee.com/cicadasmile/middle-ware-parent
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