跳至主要內容

性能优化与源码分析

cheniy2025/8/15大约 4 分钟数据库分库分表Sharding-JDBC性能优化源码分析

Sharding-JDBC 性能优化与源码分析

前置知识

在学习本教程前，请确保您已经：

  • 掌握 Sharding-JDBC 的基本使用
  • 了解 Java 并发编程
  • 熟悉 SQL 优化基础

性能优化

1. 分片策略优化

1.1 分片键选择

// 推荐：使用单一分片键
TableRuleConfiguration orderTableRuleConfig = new TableRuleConfiguration("t_order", "ds${0..1}.t_order${0..1}");
orderTableRuleConfig.setTableShardingStrategyConfig(
    new StandardShardingStrategyConfiguration("order_id", new OrderShardingAlgorithm())
);

// 不推荐：使用多分片键，可能影响性能
TableRuleConfiguration orderTableRuleConfig = new TableRuleConfiguration("t_order", "ds${0..1}.t_order${0..1}");
orderTableRuleConfig.setTableShardingStrategyConfig(
    new ComplexShardingStrategyConfiguration("order_id,user_id", new ComplexOrderShardingAlgorithm())
);

1.2 分片算法优化

public class OptimizedOrderShardingAlgorithm implements PreciseShardingAlgorithm<Long> {
    @Override
    public String doSharding(Collection<String> availableTargetNames, PreciseShardingValue<Long> shardingValue) {
        // 使用位运算代替取模运算
        long orderId = shardingValue.getValue();
        int shardingCount = availableTargetNames.size();
        int shardingKey = (int) (orderId & (shardingCount - 1));
        
        for (String tableName : availableTargetNames) {
            if (tableName.endsWith(String.valueOf(shardingKey))) {
                return tableName;
            }
        }
        throw new UnsupportedOperationException();
    }
}

2. SQL 优化

2.1 索引优化

-- 推荐：使用分片键作为查询条件
SELECT * FROM t_order WHERE order_id = ? AND user_id = ?;

-- 不推荐：不包含分片键的查询
SELECT * FROM t_order WHERE status = ?;

2.2 批量操作优化

@Service
public class OrderService {
    @Autowired
    private OrderMapper orderMapper;
    
    @Transactional
    public void batchInsert(List<Order> orders) {
        // 按分片键分组
        Map<Integer, List<Order>> shardingMap = orders.stream()
            .collect(Collectors.groupingBy(order -> 
                Math.toIntExact(order.getOrderId() % 2)));
        
        // 分组批量插入
        shardingMap.forEach((sharding, shardingOrders) -> {
            orderMapper.batchInsert(shardingOrders);
        });
    }
}

3. 连接池优化

spring:
  shardingsphere:
    datasource:
      ds0:
        type: com.zaxxer.hikari.HikariDataSource
        maximum-pool-size: 50
        minimum-idle: 10
        idle-timeout: 30000
        connection-timeout: 3000
        max-lifetime: 1800000
      ds1:
        # ... 类似配置

源码分析

1. 路由引擎

路由引擎核心源码分析
public final class PreparedStatementRoutingEngine {
    
    private final String sql;
    private final ShardingRule shardingRule;
    private final ShardingMetaData shardingMetaData;
    private final DatabaseType databaseType;
    private final List<Object> parameters;
    
    public SQLRouteResult route() {
        // 1. SQL解析
        SQLStatement sqlStatement = parse();
        
        // 2. 生成路由上下文
        RouteContext routeContext = new RouteContext(sqlStatement, parameters);
        
        // 3. 进行路由
        RouteResult routeResult = route(routeContext);
        
        // 4. 生成SQL重写上下文
        SQLRewriteContext sqlRewriteContext = createSQLRewriteContext(routeContext);
        
        // 5. 返回路由结果
        return new SQLRouteResult(sqlRewriteContext, routeResult);
    }
    
    private RouteResult route(RouteContext routeContext) {
        // 根据不同SQL类型选择路由策略
        if (routeContext.getSqlStatement() instanceof DMLStatement) {
            return routeForDML(routeContext);
        }
        if (routeContext.getSqlStatement() instanceof DDLStatement) {
            return routeForDDL(routeContext);
        }
        // ... 其他类型处理
    }
}

2. 执行引擎

执行引擎工作流程
public final class ShardingExecuteEngine implements ExecuteEngine {
    
    private final ExecutorService executorService;
    
    @Override
    public <T> List<T> execute(Collection<? extends StatementExecuteUnit> units,
                              Collection<? extends Connection> connections,
                              StatementExecuteCallback<T> callback) throws SQLException {
        // 1. 根据单元类型选择执行策略
        ExecutionStrategy strategy = ExecutionStrategyFactory.newInstance(units);
        
        // 2. 执行SQL
        return strategy.execute(units, callback);
    }
    
    private <T> List<T> executeGroup(Collection<StatementExecuteUnit> units,
                                    StatementExecuteCallback<T> callback) throws SQLException {
        // 并行执行每个分片
        return units.stream()
                .map(unit -> CompletableFuture.supplyAsync(() -> {
                    try {
                        return callback.execute(unit);
                    } catch (SQLException ex) {
                        throw new CompletionException(ex);
                    }
                }, executorService))
                .collect(Collectors.toList())
                .stream()
                .map(CompletableFuture::join)
                .collect(Collectors.toList());
    }
}

3. 结果归并

结果归并处理流程
public final class StreamingMergeEngine implements MergeEngine {
    
    private final ShardingRule shardingRule;
    private final List<QueryResult> queryResults;
    private final SQLStatement sqlStatement;
    private final List<OrderItem> orderItems;
    
    @Override
    public MergedResult merge() throws SQLException {
        if (1 == queryResults.size()) {
            return new IteratorStreamMergedResult(queryResults);
        }
        
        Map<String, Integer> labelAndIndexMap = getLabelAndIndexMap(queryResults);
        
        // 根据不同场景选择归并策略
        if (isNeedProcessGroupBy()) {
            return processGroupBy(labelAndIndexMap);
        }
        if (isNeedProcessOrderBy()) {
            return processOrderBy(labelAndIndexMap);
        }
        return processDefault(labelAndIndexMap);
    }
    
    private MergedResult processOrderBy(Map<String, Integer> labelAndIndexMap) throws SQLException {
        List<OrderItem> orderItems = new ArrayList<>(sqlStatement.getOrderByItems());
        OrderByStreamMergedResult result = new OrderByStreamMergedResult(queryResults, orderItems, labelAndIndexMap);
        return result;
    }
}

性能监控

1. 监控指标

@Configuration
public class MetricsConfiguration {
    
    @Bean
    public MeterRegistry meterRegistry() {
        return new SimpleMeterRegistry();
    }
    
    @Bean
    public ShardingMetricsAspect shardingMetricsAspect(MeterRegistry meterRegistry) {
        return new ShardingMetricsAspect(meterRegistry);
    }
}

@Aspect
@Component
public class ShardingMetricsAspect {
    
    private final MeterRegistry meterRegistry;
    
    @Around("execution(* com.example.demo.mapper.*Mapper.*(..))")    
    public Object recordMetrics(ProceedingJoinPoint joinPoint) throws Throwable {
        Timer.Sample sample = Timer.start(meterRegistry);
        try {
            return joinPoint.proceed();
        } finally {
            sample.stop(Timer.builder("sharding.sql.execution")
                .tag("class", joinPoint.getSignature().getDeclaringTypeName())
                .tag("method", joinPoint.getSignature().getName())
                .register(meterRegistry));
        }
    }
}

2. 性能分析

spring:
  shardingsphere:
    props:
      sql.show: true
      executor.size: 16
      max.connections.size.per.query: 2

最佳实践

性能优化建议

  1. 分片策略优化

    • 选择合适的分片键
    • 使用高效的分片算法
    • 避免跨分片查询

  2. SQL优化

    • 合理使用索引
    • 避免全表扫描
    • 批量操作优化

  3. 连接池优化

    • 合理配置连接池参数
    • 监控连接池状态
    • 及时释放连接

常见问题

1. 如何解决跨分片查询性能问题？

解决方案：

  1. 优化分片策略，减少跨分片查询
  2. 使用绑定表，避免笛卡尔积查询
  3. 合理设置连接池参数
  4. 使用异步查询优化
@Service
public class OrderService {
    
    @Autowired
    private OrderMapper orderMapper;
    
    public List<Order> findOrders(List<Long> orderIds) {
        // 按分片分组查询
        Map<Integer, List<Long>> shardingMap = orderIds.stream()
            .collect(Collectors.groupingBy(orderId -> 
                Math.toIntExact(orderId % 2)));
        
        // 并行查询各分片
        List<CompletableFuture<List<Order>>> futures = shardingMap.entrySet()
            .stream()
            .map(entry -> CompletableFuture.supplyAsync(() -> 
                orderMapper.findByIds(entry.getValue())))
            .collect(Collectors.toList());
        
        // 合并结果
        return futures.stream()
            .map(CompletableFuture::join)
            .flatMap(List::stream)
            .collect(Collectors.toList());
    }
}
2. 如何优化批量操作性能？

优化方案：

  1. 使用批量接口
  2. 合理设置批次大小
  3. 按分片键分组处理
  4. 使用并行处理

总结

本文深入分析了 Sharding-JDBC 的性能优化和源码实现：

  1. ✅ 性能优化：分片策略、SQL和连接池优化
  2. ✅ 源码分析：路由引擎、执行引擎和结果归并
  3. ✅ 性能监控：监控指标和性能分析
  4. ✅ 最佳实践：实用的优化建议

下一步学习

  • 深入研究分布式事务实现
  • 探索更多性能优化技巧
  • 实践监控系统搭建

希望这篇文章能帮助您更好地优化 Sharding-JDBC 的性能！如果您有任何问题，欢迎在评论区讨论。

最近更新
贡献者: cheniy