database-optimization

name	database-optimization
description	SQL query optimization and database performance specialist. Use when optimizing slow queries, fixing N+1 problems, designing indexes, implementing caching, or improving database performance. Works with PostgreSQL, MySQL, and other databases.
author	Joseph OBrien
status	unpublished
updated	2025-12-23
version	1.0.1
tag	skill
type	skill

Database Optimization

This skill optimizes database performance including query optimization, indexing strategies, N+1 problem resolution, and caching implementation.

When to Use This Skill

• When optimizing slow database queries
• When fixing N+1 query problems
• When designing indexes
• When implementing caching strategies
• When optimizing database migrations
• When improving database performance

What This Skill Does

1. Query Optimization: Analyzes and optimizes SQL queries
2. Index Design: Creates appropriate indexes
3. N+1 Resolution: Fixes N+1 query problems
4. Caching: Implements caching layers (Redis, Memcached)
5. Migration Optimization: Optimizes database migrations
6. Performance Monitoring: Sets up query performance monitoring

How to Use

Optimize Queries

Optimize this slow database query

Fix the N+1 query problem in this code

Specific Analysis

Analyze query performance and suggest indexes

Optimization Areas

Query Optimization

Techniques:

• Use EXPLAIN ANALYZE
• Optimize JOINs
• Reduce data scanned
• Use appropriate indexes
• Avoid SELECT *

Index Design

Strategies:

• Index frequently queried columns
• Composite indexes for multi-column queries
• Avoid over-indexing
• Monitor index usage
• Remove unused indexes

N+1 Problem

Pattern:

# Bad: N+1 queries
users = User.all()
for user in users:
    posts = Post.where(user_id=user.id)  # N queries

# Good: Single query with JOIN
users = User.all().includes(:posts)  # 1 query

Examples

Example 1: Query Optimization

Input: Optimize slow user query

Output:

## Database Optimization: User Query

### Current Query
```sql
SELECT * FROM users
WHERE email = '[e-mail]';
-- Execution time: 450ms

Analysis

• Full table scan (no index on email)
• Scanning 1M+ rows

Optimization

-- Add index
CREATE INDEX idx_users_email ON users(email);

-- Optimized query
SELECT id, email, name FROM users
WHERE email = '[e-mail]';
-- Execution time: 2ms

Impact

• Query time: 450ms → 2ms (99.5% improvement)
• Index size: ~50MB

## Best Practices

### Database Optimization

1. **Measure First**: Use EXPLAIN ANALYZE
2. **Index Strategically**: Not every column needs an index
3. **Monitor**: Track slow query logs
4. **Cache**: Cache expensive queries
5. **Denormalize**: When justified by read patterns

## Reference Files

- **`references/query_patterns.md`** - Common query optimization patterns, anti-patterns, and caching strategies

## Related Use Cases

- Query optimization
- Index design
- N+1 problem resolution
- Caching implementation
- Database performance improvement