Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.
Cache expensive file processing results (PDF parsing, text extraction, image analysis) using SHA-256 content hashes as cache keys. Unlike path-based caching, this approach survives file moves/renames and auto-invalidates when content changes.
--cache/--no-cache CLI optionUse file content (not path) as the cache key:
import hashlib
from pathlib import Path
_HASH_CHUNK_SIZE = 65536 # 64KB chunks for large files
def compute_file_hash(path: Path) -> str:
"""SHA-256 of file contents (chunked for large files)."""
if not path.is_file():
raise FileNotFoundError(f"File not found: {path}")
sha256 = hashlib.sha256()
with open(path, "rb") as f:
while True:
chunk = f.read(_HASH_CHUNK_SIZE)
if not chunk:
break
sha256.update(chunk)
return sha256.hexdigest()
Why content hash? File rename/move = cache hit. Content change = automatic invalidation. No index file needed.
from dataclasses import dataclass
@dataclass(frozen=True, slots=True)
class CacheEntry:
file_hash: str
source_path: str
document: ExtractedDocument # The cached result
Each cache entry is stored as {hash}.json — O(1) lookup by hash, no index file required.
import json
from typing import Any
def write_cache(cache_dir: Path, entry: CacheEntry) -> None:
cache_dir.mkdir(parents=True, exist_ok=True)
cache_file = cache_dir / f"{entry.file_hash}.json"
data = serialize_entry(entry)
cache_file.write_text(json.dumps(data, ensure_ascii=False), encoding="utf-8")
def read_cache(cache_dir: Path, file_hash: str) -> CacheEntry | None:
cache_file = cache_dir / f"{file_hash}.json"
if not cache_file.is_file():
return None
try:
raw = cache_file.read_text(encoding="utf-8")
data = json.loads(raw)
return deserialize_entry(data)
except (json.JSONDecodeError, ValueError, KeyError):
return None # Treat corruption as cache miss
Keep the processing function pure. Add caching as a separate service layer.
def extract_with_cache(
file_path: Path,
*,
cache_enabled: bool = True,
cache_dir: Path = Path(".cache"),
) -> ExtractedDocument:
"""Service layer: cache check -> extraction -> cache write."""
if not cache_enabled:
return extract_text(file_path) # Pure function, no cache knowledge
file_hash = compute_file_hash(file_path)
# Check cache
cached = read_cache(cache_dir, file_hash)
if cached is not None:
logger.info("Cache hit: %s (hash=%s)", file_path.name, file_hash[:12])
return cached.document
# Cache miss -> extract -> store
logger.info("Cache miss: %s (hash=%s)", file_path.name, file_hash[:12])
doc = extract_text(file_path)
entry = CacheEntry(file_hash=file_hash, source_path=str(file_path), document=doc)
write_cache(cache_dir, entry)
return doc
| Decision | Rationale |
|---|---|
| SHA-256 content hash | Path-independent, auto-invalidates on content change |
{hash}.json file naming | O(1) lookup, no index file needed |
| Service layer wrapper | SRP: extraction stays pure, cache is a separate concern |
| Manual JSON serialization | Full control over frozen dataclass serialization |
Corruption returns None | Graceful degradation, re-processes on next run |
cache_dir.mkdir(parents=True) | Lazy directory creation on first write |
# BAD: Path-based caching (breaks on file move/rename)
cache = {"/path/to/file.pdf": result}
# BAD: Adding cache logic inside the processing function (SRP violation)
def extract_text(path, *, cache_enabled=False, cache_dir=None):
if cache_enabled: # Now this function has two responsibilities
...
# BAD: Using dataclasses.asdict() with nested frozen dataclasses
# (can cause issues with complex nested types)
data = dataclasses.asdict(entry) # Use manual serialization instead
--cache/--no-cache options