Database Query Optimization

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

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