Copilot instructions

1---
2description: Rails Performance Guide
3applyTo: "**/*.rb"
4---
5 
6# Rails Performance Guide
7 
8## Core Philosophy
9 
10- Measure before optimizing
11- Optimize for the common case
12- Keep solutions simple and maintainable
13- Use Rails built-in performance features
14- Monitor performance in production
15 
16## Database Optimization Principles
17 
18- Design efficient schemas from the start
19- Index foreign keys and queried columns
20- Use database-specific features wisely
21- Keep queries simple and focused
22- Monitor slow query logs
23 
24## Query Optimization
25 
26- Eliminate N+1 queries with includes
27- Use select to limit returned columns
28- Implement pagination for large datasets
29- Use pluck for single column queries
30- Batch process large operations
31 
32## Indexing Strategy
33 
34- Index all foreign keys
35- Create composite indexes for multi-column queries
36- Use partial indexes for filtered queries
37- Monitor index usage and effectiveness
38- Remove unused indexes
39 
40## Caching Principles
41 
42- Cache at multiple levels
43- Use Russian doll caching for nested content
44- Implement fragment caching for expensive views
45- Cache database queries appropriately
46- Monitor cache hit rates
47 
48## Rails Cache Store
49 
50- Use Solid Cache for database-backed caching
51- Configure Redis for high-traffic applications
52- Set appropriate cache expiration
53- Use cache keys that auto-expire
54- Monitor cache memory usage
55 
56## Fragment Caching
57 
58- Cache expensive view partials
59- Use cache digests for automatic expiration
60- Implement conditional caching
61- Cache user-specific content carefully
62- Profile view rendering times
63 
64## Query Caching
65 
66- Leverage Rails query cache
67- Use low-level caching for expensive calculations
68- Cache aggregated data
69- Implement counter caches
70- Update caches strategically
71 
72## Asset Optimization
73 
74- Use Propshaft for simple asset handling
75- Implement CDN for asset delivery
76- Compress assets appropriately
77- Set proper cache headers
78- Optimize image sizes
79 
80## Background Job Performance
81 
82- Process heavy operations asynchronously
83- Use appropriate queue priorities
84- Batch similar operations
85- Monitor job queue depth
86- Implement job timeouts
87 
88## Memory Management
89 
90- Profile memory usage regularly
91- Fix memory leaks promptly
92- Use streaming for large responses
93- Implement pagination everywhere
94- Monitor memory trends
95 
96## Database Connection Pooling
97 
98- Configure connection pool appropriately
99- Monitor connection usage
100- Use read replicas for scaling
101- Implement connection timeouts
102- Handle connection failures gracefully
103 
104## Eager Loading
105 
106- Use includes for associations
107- Implement preload for separate queries
108- Use joins for filtering
109- Avoid loading unnecessary data
110- Profile association loading
111 
112## SQL Optimization
113 
114- Use EXPLAIN to analyze queries
115- Optimize slow queries first
116- Use database views for complex queries
117- Implement materialized views when needed
118- Keep queries readable
119 
120## Pagination Best Practices
121 
122- Always paginate large datasets
123- Use cursor-based pagination for APIs
124- Implement infinite scroll carefully
125- Cache pagination counts
126- Optimize count queries
127 
128## Caching Strategies
129 
130- Cache computed values
131- Use memoization for instance methods
132- Implement HTTP caching headers
133- Cache external API responses
134- Invalidate caches intelligently
135 
136## Performance Monitoring
137 
138- Use APM tools in production
139- Monitor response times
140- Track database query times
141- Alert on performance degradation
142- Profile regularly
143 
144## Load Testing
145 
146- Test with realistic data volumes
147- Simulate concurrent users
148- Identify bottlenecks early
149- Test cache effectiveness
150- Monitor resource usage
151 
152## Code Optimization
153 
154- Keep methods small and focused
155- Avoid unnecessary object allocations
156- Use efficient algorithms
157- Profile hot code paths
158- Optimize critical sections only
159 
160## View Performance
161 
162- Minimize database queries in views
163- Use streaming for large responses
164- Implement lazy loading
165- Optimize partial rendering
166- Cache complex calculations
167 
168## API Performance
169 
170- Implement response pagination
171- Use sparse fieldsets
172- Cache API responses
173- Compress responses
174- Monitor API usage
175 
176## Database Maintenance
177 
178- Run VACUUM regularly (PostgreSQL)
179- Update table statistics
180- Monitor table bloat
181- Archive old data
182- Plan capacity ahead
183 
184## Scaling Strategies
185 
186- Start with vertical scaling
187- Implement read replicas
188- Use database partitioning
189- Consider caching layers
190- Plan for horizontal scaling
191 
192## Common Bottlenecks
193 
194- N+1 queries
195- Missing indexes
196- Large result sets
197- Inefficient algorithms
198- Memory leaks
199 
200## Performance Tools
201 
202- Use Bullet gem for N+1 detection
203- Implement rack-mini-profiler
204- Use database query analyzers
205- Monitor with New Relic or similar
206- Profile with Ruby profilers
207 
208## Development Practices
209 
210- Test with production-like data
211- Profile during development
212- Review queries in pull requests
213- Monitor performance trends
214- Document performance decisions
215 
216## Performance Checklist
217 
218- [ ] All foreign keys indexed
219- [ ] N+1 queries eliminated
220- [ ] Pagination implemented
221- [ ] Caching strategy defined
222- [ ] Slow queries optimized
223- [ ] Memory usage monitored
224- [ ] Response times acceptable
225- [ ] Background jobs efficient
226- [ ] Assets optimized
227- [ ] Monitoring in place
228 
229## Best Practices Summary
230 
231- Measure before optimizing
232- Fix biggest bottlenecks first
233- Use Rails built-in features
234- Monitor production performance
235- Keep optimizations simple
236 
237Remember: Premature optimization is the root of all evil. Profile first, optimize what matters, and keep solutions maintainable.
238