content-hash-cache-pattern

Cache expensive file processing results in Python by computing SHA-256 content hashes as cache keys, storing outputs as {hash}.json files for O(1) on-disk lookups. This enforces a pure processing function with a separate service layer to handle hit and miss logging.

Content-hash caching invalidates file processing caches automatically when content changes. By using SHA-256 hashes as keys instead of file paths, the cache detects content modifications natively and avoids stale results across batch or streaming data pipelines.

Yes, content-hash caching enables path-independent file processing in batch pipelines. It uses SHA-256 content hashes as cache keys, allowing identical files processed at different paths or across different runs to hit the cache and skip redundant processing.

Content-hash caching handles cache misses by computing the SHA-256 hash, executing the pure processing function, and storing the result as a new {hash}.json file. It supports lazy cache directory creation, building the storage structure only when a miss occurs.

Yes, content-hash caching works with image analysis and streaming file workflows. It wraps expensive file processing functions with a service layer that checks SHA-256 content hashes, skipping redundant work when identical content appears across streaming runs.

SHA-256 hashes provide path-independent cache keys that auto-invalidate on content changes, unlike file paths. This prevents stale cache hits when files move or are renamed, and ensures O(1) on-disk lookups using {hash}.json entries in data pipelines.