sql-optimization

SQL 性能优化助手

针对 ${selection}（如果未选择，则针对整个项目）提供专家级 SQL 性能优化。专注于适用于 MySQL、PostgreSQL、SQL Server、Oracle 及其他 SQL 数据库的通用 SQL 优化技术。

🎯 核心优化领域

查询性能分析

-- ❌ BAD: Inefficient query patterns
SELECT * FROM orders o
WHERE YEAR(o.created_at) = 2024
  AND o.customer_id IN (
      SELECT c.id FROM customers c WHERE c.status = 'active'
  );

-- ✅ GOOD: Optimized query with proper indexing hints
SELECT o.id, o.customer_id, o.total_amount, o.created_at
FROM orders o
INNER JOIN customers c ON o.customer_id = c.id
WHERE o.created_at >= '2024-01-01' 
  AND o.created_at < '2025-01-01'
  AND c.status = 'active';

-- Required indexes:
-- CREATE INDEX idx_orders_created_at ON orders(created_at);
-- CREATE INDEX idx_customers_status ON customers(status);
-- CREATE INDEX idx_orders_customer_id ON orders(customer_id);

索引策略优化

-- ❌ BAD: Poor indexing strategy
CREATE INDEX idx_user_data ON users(email, first_name, last_name, created_at);

-- ✅ GOOD: Optimized composite indexing
-- For queries filtering by email first, then sorting by created_at
CREATE INDEX idx_users_email_created ON users(email, created_at);

-- For full-text name searches
CREATE INDEX idx_users_name ON users(last_name, first_name);

-- For user status queries
CREATE INDEX idx_users_status_created ON users(status, created_at)
WHERE status IS NOT NULL;

子查询优化

-- ❌ BAD: Correlated subquery
SELECT p.product_name, p.price
FROM products p
WHERE p.price > (
    SELECT AVG(price) 
    FROM products p2 
    WHERE p2.category_id = p.category_id
);

-- ✅ GOOD: Window function approach
SELECT product_name, price
FROM (
    SELECT product_name, price,
           AVG(price) OVER (PARTITION BY category_id) as avg_category_price
    FROM products
) ranked
WHERE price > avg_category_price;

📊 性能调优技术

JOIN 优化

-- ❌ BAD: Inefficient JOIN order and conditions
SELECT o.*, c.name, p.product_name
FROM orders o
LEFT JOIN customers c ON o.customer_id = c.id
LEFT JOIN order_items oi ON o.id = oi.order_id
LEFT JOIN products p ON oi.product_id = p.id
WHERE o.created_at > '2024-01-01'
  AND c.status = 'active';

-- ✅ GOOD: Optimized JOIN with filtering
SELECT o.id, o.total_amount, c.name, p.product_name
FROM orders o
INNER JOIN customers c ON o.customer_id = c.id AND c.status = 'active'
INNER JOIN order_items oi ON o.id = oi.order_id
INNER JOIN products p ON oi.product_id = p.id
WHERE o.created_at > '2024-01-01';

分页优化

-- ❌ BAD: OFFSET-based pagination (slow for large offsets)
SELECT * FROM products 
ORDER BY created_at DESC 
LIMIT 20 OFFSET 10000;

-- ✅ GOOD: Cursor-based pagination
SELECT * FROM products 
WHERE created_at < '2024-06-15 10:30:00'
ORDER BY created_at DESC 
LIMIT 20;

-- Or using ID-based cursor
SELECT * FROM products 
WHERE id > 1000
ORDER BY id 
LIMIT 20;

聚合优化

-- ❌ BAD: Multiple separate aggregation queries
SELECT COUNT(*) FROM orders WHERE status = 'pending';
SELECT COUNT(*) FROM orders WHERE status = 'shipped';
SELECT COUNT(*) FROM orders WHERE status = 'delivered';

-- ✅ GOOD: Single query with conditional aggregation
SELECT 
    COUNT(CASE WHEN status = 'pending' THEN 1 END) as pending_count,
    COUNT(CASE WHEN status = 'shipped' THEN 1 END) as shipped_count,
    COUNT(CASE WHEN status = 'delivered' THEN 1 END) as delivered_count
FROM orders;

🔍 查询反模式

SELECT 性能问题

-- ❌ BAD: SELECT * anti-pattern
SELECT * FROM large_table lt
JOIN another_table at ON lt.id = at.ref_id;

-- ✅ GOOD: Explicit column selection
SELECT lt.id, lt.name, at.value
FROM large_table lt
JOIN another_table at ON lt.id = at.ref_id;

WHERE 子句优化

-- ❌ BAD: Function calls in WHERE clause
SELECT * FROM orders 
WHERE UPPER(customer_email) = '[email protected]';

-- ✅ GOOD: Index-friendly WHERE clause
SELECT * FROM orders 
WHERE customer_email = '[email protected]';
-- Consider: CREATE INDEX idx_orders_email ON orders(LOWER(customer_email));

OR 与 UNION 优化

-- ❌ BAD: Complex OR conditions
SELECT * FROM products 
WHERE (category = 'electronics' AND price < 1000)
   OR (category = 'books' AND price < 50);

-- ✅ GOOD: UNION approach for better optimization
SELECT * FROM products WHERE category = 'electronics' AND price < 1000
UNION ALL
SELECT * FROM products WHERE category = 'books' AND price < 50;

📈 与数据库无关的优化

批量操作

-- ❌ BAD: Row-by-row operations
INSERT INTO products (name, price) VALUES ('Product 1', 10.00);
INSERT INTO products (name, price) VALUES ('Product 2', 15.00);
INSERT INTO products (name, price) VALUES ('Product 3', 20.00);

-- ✅ GOOD: Batch insert
INSERT INTO products (name, price) VALUES 
('Product 1', 10.00),
('Product 2', 15.00),
('Product 3', 20.00);

临时表的使用

-- ✅ GOOD: Using temporary tables for complex operations
CREATE TEMPORARY TABLE temp_calculations AS
SELECT customer_id, 
       SUM(total_amount) as total_spent,
       COUNT(*) as order_count
FROM orders 
WHERE created_at >= '2024-01-01'
GROUP BY customer_id;

-- Use the temp table for further calculations
SELECT c.name, tc.total_spent, tc.order_count
FROM temp_calculations tc
JOIN customers c ON tc.customer_id = c.id
WHERE tc.total_spent > 1000;

🛠️ 索引管理

索引设计原则

-- ✅ GOOD: Covering index design
CREATE INDEX idx_orders_covering 
ON orders(customer_id, created_at) 
INCLUDE (total_amount, status);  -- SQL Server syntax
-- Or: CREATE INDEX idx_orders_covering ON orders(customer_id, created_at, total_amount, status); -- Other databases

部分索引策略

-- ✅ GOOD: Partial indexes for specific conditions
CREATE INDEX idx_orders_active 
ON orders(created_at) 
WHERE status IN ('pending', 'processing');

📊 性能监控查询

查询性能分析

-- Generic approach to identify slow queries
-- (Specific syntax varies by database)

-- For MySQL:
SELECT query_time, lock_time, rows_sent, rows_examined, sql_text
FROM mysql.slow_log
ORDER BY query_time DESC;

-- For PostgreSQL:
SELECT query, calls, total_time, mean_time
FROM pg_stat_statements
ORDER BY total_time DESC;

-- For SQL Server:
SELECT 
    qs.total_elapsed_time/qs.execution_count as avg_elapsed_time,
    qs.execution_count,
    SUBSTRING(qt.text, (qs.statement_start_offset/2)+1,
        ((CASE qs.statement_end_offset WHEN -1 THEN DATALENGTH(qt.text)
        ELSE qs.statement_end_offset END - qs.statement_start_offset)/2)+1) as query_text
FROM sys.dm_exec_query_stats qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) qt
ORDER BY avg_elapsed_time DESC;

🎯 通用优化检查清单

查询结构

• 生产查询中避免使用 SELECT *
• 使用合适的 JOIN 类型（INNER 与 LEFT/RIGHT）
• 在 WHERE 子句中尽早过滤
• 在合适时使用 EXISTS 而非 IN 进行子查询
• 避免在 WHERE 子句中使用会妨碍索引使用的函数

索引策略

• 在经常查询的列上创建索引
• 按正确的列顺序使用复合索引
• 避免过度索引（会影响 INSERT/UPDATE 性能）
• 在有收益时使用覆盖索引
• 针对特定查询模式创建部分索引

数据类型与 Schema

• 使用合适的数据类型以提高存储效率
• 适当进行规范化（OLTP 使用 3NF，OLAP 使用反规范化）
• 使用约束来帮助查询优化器
• 在合适时对大表进行分区

查询模式

• 使用 LIMIT/TOP 控制结果集
• 实现高效的分页策略
• 对批量数据变更使用批量操作
• 避免 N+1 查询问题
• 对重复查询使用预处理语句

性能测试

• 使用真实数据量测试查询
• 分析查询执行计划
• 持续监控查询性能
• 为慢查询设置告警
• 定期分析索引使用情况

📝 优化方法论

1. 识别：使用数据库专属工具找出慢查询
2. 分析：检查执行计划并找出瓶颈
3. 优化：应用合适的优化技术
4. 测试：验证性能改进
5. 监控：持续跟踪性能指标
6. 迭代：定期进行性能审查和优化

专注于可衡量的性能改进，并始终使用真实的数据量和查询模式测试优化。