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资料
分库分表的概念及应用场景详解
分库分表带来的一些问题
sharding-jdbc水平垂直分库分表环境搭建
sharding-jdbc水平分库分表实战
sharding-jdbc垂直分库分表实战 -
垂直分库分表目的
1.主从表结构拆分,也在一定程度缓解索引性能,同时可以使主从表可以并发操作,不被行锁限制(垂直分表)
2.微服务架构情况下,为了业务更好的区分,采用多schema模式,不同模块使用不同的schema(伪垂直分库)
3.微服务架构情况下,由于某些模块业务量非常大,导致单库性能下降,影响其它模块,一般会单独拆到另外一个库中(真垂直分库) -
垂直分表实现
垂直分表就不做实验了,我们每天都在做垂直分表,比如主从表的结构 -
垂直分库实现
public class springJdbcTest { public static void main(String[] args) throws SQLException { //获取数据源 DataSource dataSource = getDataSource(); //获取jdbctemplate JdbcTemplate jdbcTemplate = getJdbcTemplate(dataSource); //执行插入语句 for(int i=1;i<=10;i++) { //执行插入用户语句 jdbcTemplate.update("insert into t_user(user_name,user_age,user_type) values (?,?,?)",i,i,i); //执行插入订单语句 jdbcTemplate.update("insert into t_order(user_id,order_price) values (?,?)",i,i); } } public static JdbcTemplate getJdbcTemplate( DataSource dataSource){ JdbcTemplate jdbcTemplate = new JdbcTemplate(); jdbcTemplate.setDataSource(dataSource); return jdbcTemplate; } public static DataSource getDataSource() throws SQLException { // 配置真实数据源 Map<String, DataSource> dataSourceMap = new HashMap<>(); // 配置第 1 个数据源 DruidDataSource dataSource1 = new DruidDataSource(); dataSource1.setDriverClassName("com.mysql.jdbc.Driver"); dataSource1.setUrl("jdbc:mysql://localhost:3306/ds0?characterEncoding=utf-8&useSSL=false"); dataSource1.setUsername("root"); dataSource1.setPassword("123456"); dataSourceMap.put("ds0", dataSource1); // 配置第 2 个数据源 DruidDataSource dataSource2 = new DruidDataSource(); dataSource2.setDriverClassName("com.mysql.jdbc.Driver"); dataSource2.setUrl("jdbc:mysql://localhost:3307/ds0?characterEncoding=utf-8&useSSL=false"); dataSource2.setUsername("root"); dataSource2.setPassword("123456"); dataSourceMap.put("ds1", dataSource2); // 配置表规则 ShardingRuleConfiguration shardingRuleConfiguration=new ShardingRuleConfiguration(); //用户表配置 //配置逻辑表和实际表分布情况,配置用户表分布情况 TableRuleConfiguration tableRuleConfiguration1 = new TableRuleConfiguration("t_user","ds0.t_user"); //生成主键策略 KeyGeneratorConfiguration keyGeneratorConfiguration1 = new KeyGeneratorConfiguration("snowflake","user_id"); //用户表的表和库路由配置,都是单库单表情况,相当于多数据源情况,库和表都是固定 ShardingStrategyConfiguration shardingStrategyConfiguration1 = new InlineShardingStrategyConfiguration("user_id","t_user"); ShardingStrategyConfiguration shardingStrategyConfiguration2 = new InlineShardingStrategyConfiguration("user_id","ds0"); tableRuleConfiguration1.setKeyGeneratorConfig(keyGeneratorConfiguration1); tableRuleConfiguration1.setTableShardingStrategyConfig(shardingStrategyConfiguration1); tableRuleConfiguration1.setDatabaseShardingStrategyConfig(shardingStrategyConfiguration2); //订单表配置 //配置逻辑表和实际表分布情况,配置订单表分布情况 TableRuleConfiguration tableRuleConfiguration2 = new TableRuleConfiguration("t_order","ds1.t_order"); //生成主键策略 KeyGeneratorConfiguration keyGeneratorConfiguration2 = new KeyGeneratorConfiguration("snowflake","order_id"); //订单表的表和库路由配置,都是单库单表情况,相当于多数据源情况,库和表都是固定 ShardingStrategyConfiguration shardingStrategyConfiguration3 = new InlineShardingStrategyConfiguration("order_id","t_order"); ShardingStrategyConfiguration shardingStrategyConfiguration4 = new InlineShardingStrategyConfiguration("order_id","ds1"); tableRuleConfiguration2.setKeyGeneratorConfig(keyGeneratorConfiguration2); tableRuleConfiguration2.setTableShardingStrategyConfig(shardingStrategyConfiguration3); tableRuleConfiguration2.setDatabaseShardingStrategyConfig(shardingStrategyConfiguration4); //实际表生成主键策略 shardingRuleConfiguration.setTableRuleConfigs(Arrays.asList(tableRuleConfiguration1,tableRuleConfiguration2)); return ShardingDataSourceFactory.createDataSource(dataSourceMap, shardingRuleConfiguration, new Properties()); } }
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垂直分库效果
sharding-jdbc垂直分库分表实战
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