Implement caching layer for performance optimization

[andhijeannot]

Overview

Implement comprehensive caching layer to optimize performance for large music libraries and frequent operations.

Scope

Create intelligent caching system that dramatically improves response times while managing memory efficiently.

Implementation Requirements

Cache Infrastructure (infrastructure/cache/)

• LRU cache implementation with configurable size limits
• TTL-based cache expiration
• Cache invalidation strategies
• Multi-level caching (memory + optional disk)
• Cache warming and preloading
• Cache statistics and monitoring

Library Data Caching

• Track metadata caching with smart prefetching
• Search result caching with query normalization
• Playlist caching with dependency tracking
• Artist/album aggregation caching
• Library statistics caching

Cache Strategies

• Page-based caching for large datasets
• Predictive caching based on usage patterns
• Intelligent cache warming on startup
• Background cache refresh
• Cache coherency across multiple clients

Performance Optimization

• Sub-100ms cache hits for common operations
• Memory usage limits (100MB default, 1GB max)
• Cache hit ratio monitoring and optimization
• Automatic cache tuning based on usage
• Low memory pressure cache eviction

Technical Requirements

1. Thread Safety - Concurrent cache access
2. Memory Management - Configurable limits and pressure handling
3. Performance - <1ms cache hit times
4. Reliability - Graceful degradation when cache unavailable
5. Monitoring - Comprehensive cache metrics
6. Configuration - Flexible cache policies

Cache Invalidation

• Time-based expiration (5-minute default TTL)
• Event-based invalidation on library changes
• Manual cache refresh capabilities
• Selective cache invalidation
• Cache warming after invalidation

Success Criteria

• LRU cache implementation with TTL support
• Library data caching reduces query times by 90%
• Memory usage stays within configured limits
• Cache hit ratios >80% for common operations
• Cache invalidation works correctly
• Performance tests demonstrate improvement
• Cache statistics provide useful insights
• Integration with all repository implementations

Performance Targets

• Cache hit: <1ms response time
• Cache miss: <500ms (original operation time)
• Memory usage: 100MB typical, 1GB maximum
• Cache hit ratio: >80% for steady-state operations
• Startup time: <2s with cache warming

Testing Strategy

• Performance benchmarking with and without cache
• Memory usage profiling under various loads
• Cache invalidation correctness testing
• Concurrent access stress testing
• Cache statistics accuracy verification
• Large library performance testing