本文介绍了spring boot使用sharding jdbc的配置方式，分享给大家，具体如下：

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); } }

配置的多个数据源交给sharding-jdbc管理，sharding-jdbc创建一个DataSource数据源提供给mybatis使用

官方文档：http://shardingjdbc.io/index_zh.html

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

@Beanpublic 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

@Beanpublic 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@Beanpublic 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

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。