Database Query Optimization
Table of Contents
Overview
Slow database queries are a common performance bottleneck. Optimization through indexing, efficient queries, and caching dramatically improves application performance.
When to Use
- Slow response times
- High database CPU usage
- Performance regression
- New feature deployment
- Regular maintenance
Quick Start
Minimal working example:
-- Analyze query performance
EXPLAIN ANALYZE
SELECT users.id, users.name, COUNT(orders.id) as order_count
FROM users
LEFT JOIN orders ON users.id = orders.user_id
WHERE users.created_at > '2024-01-01'
GROUP BY users.id, users.name
ORDER BY order_count DESC;
-- Results show:
-- - Seq Scan (slow) vs Index Scan (fast)
-- - Rows: actual vs planned (high variance = bad)
-- - Execution time (milliseconds)
-- Key metrics:
-- - Sequential Scan: Full table read (slow)
-- - Index Scan: Uses index (fast)
-- - Nested Loop: Joins with loops
-- - Sort: In-memory or disk sort
Reference Guides
Detailed implementations in the references/ directory:
| Guide | Contents | |---|---| | Query Analysis | Query Analysis | | Indexing Strategy | Indexing Strategy | | Query Optimization Techniques | Query Optimization Techniques | | Optimization Checklist | Optimization Checklist |
Best Practices
✅ DO
- Follow established patterns and conventions
- Write clean, maintainable code
- Add appropriate documentation
- Test thoroughly before deploying
❌ DON'T
- Skip testing or validation
- Ignore error handling
- Hard-code configuration values