Content Hash Cache Pattern

作者 affaan-mef648e01899b无许可证275K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库3天前更新

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

仅含说明Software Development

AI 生成的概览

介绍基于内容哈希的文件缓存模式，用于缓存耗时的文件处理结果。

功能: 该技能记录了一种设计模式：使用 SHA-256 内容哈希作为缓存键，缓存 PDF 解析、文本提取等耗时的文件处理结果。它说明了如何分块计算文件哈希、将每个结果以 JSON 缓存条目存储，并用服务层包装纯提取函数，在提取前先检查缓存。文中还列出了设计取舍、最佳实践和反模式。
适用场景: 适用于构建文件处理流水线、同一批文件会被反复处理的场景，且希望结果在文件移动或重命名后仍然有效。也适合需要缓存开关的 CLI 工具，以及在不修改现有纯函数的前提下为其添加缓存的项目。
运行要求: 不附带脚本，仅为说明文档。示例假定使用 Python 及其标准库模块 hashlib、json、pathlib 和 dataclasses，并需要一个日志记录器和已有的提取函数。

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)

来源与署名

来源：affaan-m/ecc位于.kiro/skills/content-hash-cache-pattern提交ef648e0

许可证：无许可证

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架

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

来源与署名

更多来自 affaan-m/ecc 的技能

Vue Patterns

Uncloud

Team Agent Orchestration

Tasteforge Video

Taste

Social Publisher

更多Software Development技能

Playground

M5 Onboard

Cardputer Buddy

Testing Strategy

Tech Debt

System Design