Content Hash Cache Pattern

作者 affaan-mef648e01899b無授權條款275K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫3 天前更新

Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.

僅含說明Software Development

AI 產生的概覽

介紹以內容雜湊為基礎的檔案快取模式，用來快取耗時的檔案處理結果。

功能: 這個技能說明一種設計模式：以 SHA-256 內容雜湊作為快取鍵，快取 PDF 解析、文字擷取等耗時的檔案處理結果。內容說明如何分塊計算檔案雜湊、把每筆結果存成 JSON 快取項目，並以服務層包裝純擷取函式，在擷取前先檢查快取。文中也列出設計取捨、最佳實務與反模式。
適用情境: 適合用於建立檔案處理流程、同一批檔案會反覆處理，且希望結果在檔案搬移或重新命名後仍然有效的情況。也適合需要快取開關的 CLI 工具，以及在不修改現有純函式的前提下為其加上快取的專案。
執行需求: 不附指令碼，僅為說明文件。範例假定使用 Python 及其標準函式庫模組 hashlib、json、pathlib 與 dataclasses，並需要一個日誌記錄器與既有的擷取函式。

Content-Hash File Cache Pattern

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.

When to Activate

Building file processing pipelines (PDF, images, text extraction)
Processing cost is high and same files are processed repeatedly
Need a --cache/--no-cache CLI option
Want to add caching to existing pure functions without modifying them

Core Pattern

1. Content-Hash-Based Cache Key

Use file content (not path) as the cache key:

python

import hashlibfrom 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()

import hashlibfrom 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.

2. Frozen Dataclass for Cache Entry

python

from dataclasses import dataclass
@dataclass(frozen=True, slots=True)class CacheEntry:    file_hash: str    source_path: str    document: ExtractedDocument  # The cached result

from dataclasses import dataclass
@dataclass(frozen=True, slots=True)class CacheEntry:    file_hash: str    source_path: str    document: ExtractedDocument  # The cached result

3. File-Based Cache Storage

Each cache entry is stored as {hash}.json — O(1) lookup by hash, no index file required.

python

import jsonfrom 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

import jsonfrom 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

4. Service Layer Wrapper (SRP)

Keep the processing function pure. Add caching as a separate service layer.

python

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

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

Key Design Decisions

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

Best Practices

Hash content, not paths — paths change, content identity doesn't
Chunk large files when hashing — avoid loading entire files into memory
Keep processing functions pure — they should know nothing about caching
Log cache hit/miss with truncated hashes for debugging
Handle corruption gracefully — treat invalid cache entries as misses, never crash

Anti-Patterns to Avoid

python

# 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

# 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

When to Use

File processing pipelines (PDF parsing, OCR, text extraction, image analysis)
CLI tools that benefit from --cache/--no-cache options
Batch processing where the same files appear across runs
Adding caching to existing pure functions without modifying them

When NOT to Use

Data that must always be fresh (real-time feeds)
Cache entries that would be extremely large (consider streaming instead)
Results that depend on parameters beyond file content (e.g., different extraction configs)

來源與署名

來源：affaan-m/ecc位於.kiro/skills/content-hash-cache-pattern提交ef648e0

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檢舉或申請下架

Content Hash Cache Pattern

Content-Hash File Cache Pattern

When to Activate

Core Pattern

1. Content-Hash-Based Cache Key

2. Frozen Dataclass for Cache Entry

3. File-Based Cache Storage

4. Service Layer Wrapper (SRP)

Key Design Decisions

Best Practices

Anti-Patterns to Avoid

When to Use

When NOT to Use

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