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spring boot使用sharding jdbc的配置方式 原 荐 spring boot使用sharding jdbc的配置方式 J猿



说明

  • 要排除DataSourceAutoConfiguration,否则多数据源无法配置
@SpringBootApplication
@EnableAutoConfiguration(exclude={DataSourceAutoConfiguration.class})
public class Application {

    public static void main(String[] args) {
    	SpringApplication.run(Application.class, args);
    }
	
}

步骤

  • 配置多个数据源,数据源的名称最好要有一定的规则,方便配置分库的计算规则
@Bean(initMethod="init", destroyMethod="close", name="dataSource0")
@ConfigurationProperties(prefix = "spring.datasource")
public DataSource dataSource0(){
    return new DruidDataSource();
}

@Bean(initMethod="init", destroyMethod="close", name="dataSource1")
@ConfigurationProperties(prefix = "spring.datasource2")
public DataSource dataSource1(){
    return new DruidDataSource();
}
  • 配置数据源规则,即将多个数据源交给sharding-jdbc管理,并且可以设置默认的数据源,当表没有配置分库规则时会使用默认的数据源
@Bean
public DataSourceRule dataSourceRule(@Qualifier("dataSource0") DataSource dataSource0, 
        @Qualifier("dataSource1") DataSource dataSource1){
    Map<String, DataSource> dataSourceMap = new HashMap<>();
    dataSourceMap.put("dataSource0", dataSource0);
    dataSourceMap.put("dataSource1", dataSource1);
    return new DataSourceRule(dataSourceMap, "dataSource0");
}
  • 配置数据源策略和表策略,具体策略需要自己实现
@Bean
public ShardingRule shardingRule(DataSourceRule dataSourceRule){
    //表策略
    TableRule orderTableRule = TableRule.builder("t_order")
            .actualTables(Arrays.asList("t_order_0", "t_order_1"))
            .tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm()))
            .dataSourceRule(dataSourceRule)
            .build();
    TableRule orderItemTableRule = TableRule.builder("t_order_item")
            .actualTables(Arrays.asList("t_order_item_0", "t_order_item_1"))
            .tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm()))
            .dataSourceRule(dataSourceRule)
            .build();
    //绑定表策略,在查询时会使用主表策略计算路由的数据源,因此需要约定绑定表策略的表的规则需要一致,可以一定程度提高效率
    List<BindingTableRule> bindingTableRules = new ArrayList<BindingTableRule>();
    bindingTableRules.add(new BindingTableRule(Arrays.asList(orderTableRule, orderItemTableRule)));
    return ShardingRule.builder()
            .dataSourceRule(dataSourceRule)
            .tableRules(Arrays.asList(orderTableRule, orderItemTableRule))
            .bindingTableRules(bindingTableRules)
            .databaseShardingStrategy(new DatabaseShardingStrategy("user_id", new ModuloDatabaseShardingAlgorithm()))
            .tableShardingStrategy(new TableShardingStrategy("order_id", new ModuloTableShardingAlgorithm()))
            .build();
}
  • 创建sharding-jdbc的数据源DataSource,MybatisAutoConfiguration会使用此数据源
@Bean("dataSource")
public DataSource shardingDataSource(ShardingRule shardingRule){
    return ShardingDataSourceFactory.createDataSource(shardingRule);
}
  • 需要手动配置事务管理器(原因未知)
//需要手动声明配置事务
@Bean
public DataSourceTransactionManager transactitonManager(@Qualifier("dataSource") DataSource dataSource){
    return new DataSourceTransactionManager(dataSource);
}
  • 分库策略的简单实现,接口:DatabaseShardingAlgorithm
import java.util.Collection;
import java.util.LinkedHashSet;

import com.dangdang.ddframe.rdb.sharding.api.ShardingValue;
import com.dangdang.ddframe.rdb.sharding.api.strategy.database.SingleKeyDatabaseShardingAlgorithm;
import com.google.common.collect.Range;

/**
 * Created by fuwei.deng on 2017年5月11日.
 */
public class ModuloDatabaseShardingAlgorithm implements SingleKeyDatabaseShardingAlgorithm<Long> {

    @Override
    public String doEqualSharding(Collection<String> databaseNames, ShardingValue<Long> shardingValue) {
    	for (String each : databaseNames) {
            if (each.endsWith(shardingValue.getValue() % 2 + "")) {
                return each;
            }
        }
        throw new IllegalArgumentException();
    }
    
    @Override
    public Collection<String> doInSharding(Collection<String> databaseNames, ShardingValue<Long> shardingValue) {
    	Collection<String> result = new LinkedHashSet<>(databaseNames.size());
        for (Long value : shardingValue.getValues()) {
            for (String tableName : databaseNames) {
                if (tableName.endsWith(value % 2 + "")) {
                    result.add(tableName);
                }
            }
        }
        return result;
    }
    
    @Override
    public Collection<String> doBetweenSharding(Collection<String> databaseNames, ShardingValue<Long> shardingValue) {
    	Collection<String> result = new LinkedHashSet<>(databaseNames.size());
        Range<Long> range = (Range<Long>) shardingValue.getValueRange();
        for (Long i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
            for (String each : databaseNames) {
                if (each.endsWith(i % 2 + "")) {
                    result.add(each);
                }
            }
        }
        return result;
    }

}
  • 分表策略的基本实现,接口:TableShardingAlgorithm
import java.util.Collection;
import java.util.LinkedHashSet;

import com.dangdang.ddframe.rdb.sharding.api.ShardingValue;
import com.dangdang.ddframe.rdb.sharding.api.strategy.table.SingleKeyTableShardingAlgorithm;
import com.google.common.collect.Range;

/**
 * Created by fuwei.deng on 2017年5月11日.
 */
public class ModuloTableShardingAlgorithm implements SingleKeyTableShardingAlgorithm<Long> {

    @Override
    public String doEqualSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
    	for (String each : tableNames) {
            if (each.endsWith(shardingValue.getValue() % 2 + "")) {
                return each;
            }
        }
        throw new IllegalArgumentException();
    }
    
    @Override
    public Collection<String> doInSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
    	Collection<String> result = new LinkedHashSet<>(tableNames.size());
        for (Long value : shardingValue.getValues()) {
            for (String tableName : tableNames) {
                if (tableName.endsWith(value % 2 + "")) {
                    result.add(tableName);
                }
            }
        }
        return result;
    }
    
    @Override
    public Collection<String> doBetweenSharding(Collection<String> tableNames, ShardingValue<Long> shardingValue) {
    	Collection<String> result = new LinkedHashSet<>(tableNames.size());
        Range<Long> range = (Range<Long>) shardingValue.getValueRange();
        for (Long i = range.lowerEndpoint(); i <= range.upperEndpoint(); i++) {
            for (String each : tableNames) {
                if (each.endsWith(i % 2 + "")) {
                    result.add(each);
                }
            }
        }
        return result;
    }

}
  • 至此,分库分表的功能已经实现

读写分离

  • 读写分离需在创建DataSourceRule之前加一层主从数据源的创建
// 构建读写分离数据源, 读写分离数据源实现了DataSource接口, 可直接当做数据源处理. 
// masterDataSource0, slaveDataSource00, slaveDataSource01等为使用DBCP等连接池配置的真实数据源
DataSource masterSlaveDs0 = MasterSlaveDataSourceFactory.createDataSource("ms_0", 
                    masterDataSource0, slaveDataSource00, slaveDataSource01);
DataSource masterSlaveDs1 = MasterSlaveDataSourceFactory.createDataSource("ms_1", 
                    masterDataSource1, slaveDataSource11, slaveDataSource11);

// 构建分库分表数据源
Map<String, DataSource> dataSourceMap = new HashMap<>(2);
dataSourceMap.put("ms_0", masterSlaveDs0);
dataSourceMap.put("ms_1", masterSlaveDs1);

// 通过ShardingDataSourceFactory继续创建ShardingDataSource
  • 强制使用主库时
HintManager hintManager = HintManager.getInstance();
hintManager.setMasterRouteOnly();
// 继续JDBC操作

强制路由

  • 使用ThreadLocal机制实现,在执行数据库操作之前通过HintManager改变用于计算路由的值
  • 设置HintManager的时候分库和分表的策略必须同时设置,并且设置后需要路由的表都需要设置用于计算路由的值。比如强制路由后需要操作t_order和t_order_item两个表,那么两个表的分库和分表的策略都需要设置
HintManager hintManager = HintManager.getInstance();
hintManager.addDatabaseShardingValue("t_order", "user_id", 1L);
hintManager.addTableShardingValue("t_order", "order_id", order.getOrderId());
hintManager.addDatabaseShardingValue("t_order_item", "user_id", 1L);
hintManager.addTableShardingValue("t_order_item", "order_id", order.getOrderId());

事务

  • sharding-jdbc-transaction实现柔性事务(默认提供了基于内存的事务日志存储器和内嵌异步作业),可结合elastic-job(sharding-jdbc-transaction-async-job)实现异步柔性事务
  • 没有与spring结合使用的方式,需要自己封装