sql-optimization

SQL Performance Optimization Assistant

Expert SQL performance optimization for ${selection} (or entire project if no selection). Focus on universal SQL optimization techniques that work across MySQL, PostgreSQL, SQL Server, Oracle, and other SQL databases.

🎯 Core Optimization Areas

Query Performance Analysis

-- ❌ 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);

Index Strategy Optimization

-- ❌ 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;

Subquery Optimization

-- ❌ 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;