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A horizontal split
1, the level of sub-libraries
1), the concept:
to field basis, according to a certain policy, split the data into a database of multiple libraries.
2), the results of
the structure are the same in each library; data not the same;
All libraries and the whole set of data amount;
2, the horizontal sub-table
1), the concept
in the field basis, according to a certain policy, split the data into a table of multiple tables.
2), the results of
the structure of each table are the same; data not the same;
all tables are set and the total amount of data;
Two, Shard-jdbc middleware
1, Chart
2. Features
1), Sharding-JDBC encapsulates JDBC API, legacy code migration almost zero cost.
2), applicable to any Java-based ORM framework, such as Hibernate, Mybatis like.
3), it may be based on any third party database connection pool, such as DBCP, C3P0, BoneCP, Druid like.
4) to provide a service to jar package, no proxy proxy layer, no additional deployment, no other dependencies.
5), flexible partitioning strategy, to support the equal sign, between, in other multi-dimensional slices, also support Multiply key.
6), SQL parsing functional, supports aggregation, grouping, sorting, limit, or other inquiries.
Third, the project presentation
1, project structure
springboot 2.0 版本
druid 1.1.13 版本
sharding-jdbc 3.1 版本2, database configuration
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_two3, the core block
Data source configuration file
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=5000Sub-library database strategy
/**
* 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) ;
}
}Data Table 1 sub-table strategy
/**
* 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);
}
}Data Table 2 sub-table strategy
/**
* 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);
}
}Data Source integrated configuration
/**
* 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
*/
@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;
}
}Test code execution flow
@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 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);
}
}Fourth, the project source code
GitHub·地址
https://github.com/cicadasmile/middle-ware-parent
GitEE·地址
https://gitee.com/cicadasmile/middle-ware-parent