--- description: Rails Performance Guide applyTo: "**/*.rb" --- # Rails Performance Guide ## Core Philosophy - Measure before optimizing - Optimize for the common case - Keep solutions simple and maintainable - Use Rails built-in performance features - Monitor performance in production ## Database Optimization Principles - Design efficient schemas from the start - Index foreign keys and queried columns - Use database-specific features wisely - Keep queries simple and focused - Monitor slow query logs ## Query Optimization - Eliminate N+1 queries with includes - Use select to limit returned columns - Implement pagination for large datasets - Use pluck for single column queries - Batch process large operations ## Indexing Strategy - Index all foreign keys - Create composite indexes for multi-column queries - Use partial indexes for filtered queries - Monitor index usage and effectiveness - Remove unused indexes ## Caching Principles - Cache at multiple levels - Use Russian doll caching for nested content - Implement fragment caching for expensive views - Cache database queries appropriately - Monitor cache hit rates ## Rails Cache Store - Use Solid Cache for database-backed caching - Configure Redis for high-traffic applications - Set appropriate cache expiration - Use cache keys that auto-expire - Monitor cache memory usage ## Fragment Caching - Cache expensive view partials - Use cache digests for automatic expiration - Implement conditional caching - Cache user-specific content carefully - Profile view rendering times ## Query Caching - Leverage Rails query cache - Use low-level caching for expensive calculations - Cache aggregated data - Implement counter caches - Update caches strategically ## Asset Optimization - Use Propshaft for simple asset handling - Implement CDN for asset delivery - Compress assets appropriately - Set proper cache headers - Optimize image sizes ## Background Job Performance - Process heavy operations asynchronously - Use appropriate queue priorities - Batch similar operations - Monitor job queue depth - Implement job timeouts ## Memory Management - Profile memory usage regularly - Fix memory leaks promptly - Use streaming for large responses - Implement pagination everywhere - Monitor memory trends ## Database Connection Pooling - Configure connection pool appropriately - Monitor connection usage - Use read replicas for scaling - Implement connection timeouts - Handle connection failures gracefully ## Eager Loading - Use includes for associations - Implement preload for separate queries - Use joins for filtering - Avoid loading unnecessary data - Profile association loading ## SQL Optimization - Use EXPLAIN to analyze queries - Optimize slow queries first - Use database views for complex queries - Implement materialized views when needed - Keep queries readable ## Pagination Best Practices - Always paginate large datasets - Use cursor-based pagination for APIs - Implement infinite scroll carefully - Cache pagination counts - Optimize count queries ## Caching Strategies - Cache computed values - Use memoization for instance methods - Implement HTTP caching headers - Cache external API responses - Invalidate caches intelligently ## Performance Monitoring - Use APM tools in production - Monitor response times - Track database query times - Alert on performance degradation - Profile regularly ## Load Testing - Test with realistic data volumes - Simulate concurrent users - Identify bottlenecks early - Test cache effectiveness - Monitor resource usage ## Code Optimization - Keep methods small and focused - Avoid unnecessary object allocations - Use efficient algorithms - Profile hot code paths - Optimize critical sections only ## View Performance - Minimize database queries in views - Use streaming for large responses - Implement lazy loading - Optimize partial rendering - Cache complex calculations ## API Performance - Implement response pagination - Use sparse fieldsets - Cache API responses - Compress responses - Monitor API usage ## Database Maintenance - Run VACUUM regularly (PostgreSQL) - Update table statistics - Monitor table bloat - Archive old data - Plan capacity ahead ## Scaling Strategies - Start with vertical scaling - Implement read replicas - Use database partitioning - Consider caching layers - Plan for horizontal scaling ## Common Bottlenecks - N+1 queries - Missing indexes - Large result sets - Inefficient algorithms - Memory leaks ## Performance Tools - Use Bullet gem for N+1 detection - Implement rack-mini-profiler - Use database query analyzers - Monitor with New Relic or similar - Profile with Ruby profilers ## Development Practices - Test with production-like data - Profile during development - Review queries in pull requests - Monitor performance trends - Document performance decisions ## Performance Checklist - [ ] All foreign keys indexed - [ ] N+1 queries eliminated - [ ] Pagination implemented - [ ] Caching strategy defined - [ ] Slow queries optimized - [ ] Memory usage monitored - [ ] Response times acceptable - [ ] Background jobs efficient - [ ] Assets optimized - [ ] Monitoring in place ## Best Practices Summary - Measure before optimizing - Fix biggest bottlenecks first - Use Rails built-in features - Monitor production performance - Keep optimizations simple Remember: Premature optimization is the root of all evil. Profile first, optimize what matters, and keep solutions maintainable.