1. Horizontal division
1. Horizontal sub-database
1) Concept:
Based on fields, according to a certain strategy, the data in one library is split into multiple libraries.
2). Results
The structure of each library is the same; the data is different;
the union of all the libraries is the full amount of data;
2, the level is divided into tables
1), the concept
is based on the field, according to a certain strategy, the data in a table Split into multiple tables.
2). Results
The structure of each table is the same; the data is different;
the union of all tables is the full amount of data;
2. Shard-jdbc middleware
1. Architecture diagram
2. Features
1) Sharding-JDBC directly encapsulates the JDBC API, and the migration cost of the old code is almost zero.
2) Suitable for any Java-based ORM framework, such as Hibernate, Mybatis, etc.
3), can be based on any third-party database connection pool, such as DBCP, C3P0, BoneCP, Druid, etc.
4) Provide services in the form of jar packages, no proxy agent layer, no additional deployment, no other dependencies.
5) Flexible sharding strategy, can support multi-dimensional sharding such as equal sign, between, in, etc., and can also support multiple sharding keys.
6) The SQL parsing function is complete, supporting queries such as aggregation, grouping, sorting, limit, or.
3. Project demonstration
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_two
3. Core code block
1), 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=5000
2), database sub-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) ;
}
}
3), 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);
}
}
4), 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);
}
}
5), data source integration 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;
}
}
6), test code execution process
@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);
}
}