database-optimization
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.
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
View source ↗GuidesSkill instructions
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
- Query Optimization: Analyzes and optimizes SQL queries
- Index Design: Creates appropriate indexes
- N+1 Resolution: Fixes N+1 query problems
- Caching: Implements caching layers (Redis, Memcached)
- Migration Optimization: Optimizes database migrations
- 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 = 'user@example.com';
-- 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 = 'user@example.com';
-- 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
Examples
Read the usage examples in the source repository.
View source ↗Source history
- Indexed on
- 2026-01-20
- Index updated
- 2026-01-28
- Branch
- main
Index and sync dates describe this directory. View the source repository for code history.
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