Why use content hashes instead of file paths for caching?

Content hashes ensure that if a file is moved or renamed, the cache remains valid. It also guarantees that if even a single byte of the content changes, the cache is automatically invalidated, preventing stale data.

How does this pattern handle memory usage for large files?

The pattern implements chunked reading (typically in 64KB blocks) during the hashing process. This allows it to generate SHA-256 signatures for very large files without loading the entire content into RAM.

Does this skill require a database to manage the cache?

No, it uses a flat-file storage system where results are saved as JSON files named after their hash. This provides O(1) lookup speed using standard filesystem operations without the overhead of a database.

Is it easy to integrate with my existing Python code?

Yes. By using the recommended service-layer wrapper, you can wrap any existing pure function with caching logic without having to modify the original function's code.

Content-Hash Caching Pattern

Name: Content-Hash Caching Pattern
Author: flatrick

byflatrick

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Herramientas para Desarrolladores

Implements path-independent, auto-invalidating cache systems for expensive file processing tasks using SHA-256 content hashes.

This skill provides a robust architecture for caching the results of high-latency file operations such as PDF parsing, OCR, and AI-driven image analysis. By utilizing SHA-256 content hashes as cache keys instead of file paths, the system ensures that renamed or moved files still result in cache hits, while any modification to file content triggers automatic invalidation. It follows clean architecture principles by implementing a service-layer wrapper, allowing developers to add high-performance caching to existing pure functions without violating the Single Responsibility Principle.

Características Principales

01SHA-256 content-based hashing for path-independent caching

02O(1) file-based lookup system with no central index required

03Chunked file processing to handle large assets without memory spikes

04Service layer separation to keep processing logic pure

05Graceful degradation that treats corrupted cache files as misses

060 GitHub stars

Casos de Uso

01CLI tools that require high-performance --cache and --no-cache options

02Document processing pipelines involving expensive PDF or text extraction

03Batch AI analysis of image or media assets where files are frequently moved

Características Principales

01SHA-256 content-based hashing for path-independent caching

02O(1) file-based lookup system with no central index required

03Chunked file processing to handle large assets without memory spikes

04Service layer separation to keep processing logic pure

05Graceful degradation that treats corrupted cache files as misses

060 GitHub stars

Casos de Uso

01CLI tools that require high-performance --cache and --no-cache options

02Document processing pipelines involving expensive PDF or text extraction

03Batch AI analysis of image or media assets where files are frequently moved