mirror of
https://github.com/beshu-tech/deltaglider.git
synced 2026-03-18 07:23:42 +01:00
47f022fffe
Implements cache clearing functionality for SDK users who need manual cache management in long-running applications where automatic cleanup on process exit is not sufficient. New Features: - Added `clear()` method to CachePort protocol - Implemented `clear()` in all cache adapters: * ContentAddressedCache: Clears files and SHA mappings * EncryptedCache: Clears encryption mappings and delegates to backend * MemoryCache: Already had clear() method - Added `clear_cache()` method to DeltaGliderClient for public API Cache Management API: ```python from deltaglider import create_client client = create_client() # Upload files client.put_object(Bucket='bucket', Key='file.zip', Body=data) # Clear cache manually (important for long-running apps!) client.clear_cache() ``` New Documentation: - docs/CACHE_MANAGEMENT.md (684 lines) * Comprehensive guide for programmatic cache management * Long-running application strategies (web apps, services, batch jobs) * Encryption key management (ephemeral vs. persistent) * Key rotation procedures * Memory vs. filesystem cache trade-offs * Best practices by application type * Monitoring and troubleshooting Key Topics Covered: - Why SDK requires manual cache management (vs. CLI auto-cleanup) - When to clear cache (periodic, config changes, tests, etc.) - Cache strategies for 5 application types: * Long-running background services * Periodic batch jobs * Web applications / API servers * Testing / CI/CD * AWS Lambda / Serverless - Encryption key management: * Ephemeral keys (default, maximum security) * Persistent keys (shared cache scenarios) * Key rotation procedures * Secure key storage (Secrets Manager) - Memory vs. filesystem cache selection - Monitoring cache health - Troubleshooting common issues Use Cases: - Long-running services: Periodic cache clearing to prevent growth - Batch jobs: Clear cache in finally block - Tests: Clear cache after each test for clean state - Multi-process: Shared cache with persistent encryption keys - High performance: Memory cache with automatic LRU eviction Security Enhancements: - Documented encryption key lifecycle management - Key rotation procedures - Secure key storage best practices - Ephemeral vs. persistent key trade-offs Testing: - All 119 tests passing ✅ - Type checking: 0 errors (mypy) ✅ - Linting: All checks passed (ruff) ✅ Breaking Changes: None (new API only) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
