emboss 0.4.0

On-Disk Input-keyed Cache — disk-backed memoization with pydantic-aware encoding

emboss

On-Disk Input-keyed Cache — disk-backed memoization with pydantic-aware encoding.

Version: 0.2.0

pip install emboss              # core (just diskcache)
pip install emboss[pydantic]    # + pydantic v2 BaseModel support

Why

functools.lru_cache is per-process. diskcache survives invocations but pickles values as-is — which breaks the moment your cached return type is a pydantic BaseModel defined in __main__ (the new process can't unpickle __main__.MyModel). emboss fixes that by detecting BaseModel return annotations and converting to/from plain dicts at the cache boundary.

Plus: a None-aware sentinel so functions returning None actually cache instead of re-running every call.

Quick start

import diskcache
from emboss import cached

cache = diskcache.Cache("/tmp/my-cache")

@cached(cache)
def fetch(url: str) -> dict:
    import requests
    return requests.get(url).json()

fetch("https://api.example.com/users/1")  # network
fetch("https://api.example.com/users/1")  # cached, no network

Default cache directory

Passing a diskcache.Cache is optional. With @cached() (no cache argument), emboss creates one at the directory named by the EMBOSS_CACHE_DIR environment variable; if unset, diskcache falls back to a temporary directory.

EMBOSS_CACHE_DIR=.data/cache python my_script.py

The cache location never affects keying — keys are function source + arguments either way.

Pydantic BaseModel returns

emboss reads the function's return type annotation. If it sees a BaseModel, list[BaseModel], dict[str, BaseModel], or BaseModel | None, it serialises via model.model_dump() before pickling and rehydrates via Model.model_validate(...) on read. The cached value on disk is a plain dict — round-trips cleanly across process boundaries, even for models defined in __main__.

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str

@cached(cache)
def get_user(uid: int) -> User | None:
    ...

@cached(cache)
def list_users() -> list[User]:
    ...

@cached(cache)
def users_by_id() -> dict[str, User]:
    ...

Functions returning non-BaseModel types continue to pickle as-is — fully backward-compatible.

None caching

@cached(cache)
def lookup(query: str) -> str | None:
    return external_api(query)

lookup("missing")  # returns None, cached
lookup("missing")  # returns cached None, no re-run

The previous behaviour (skip-cache-on-None) is replaced by a _MISSING sentinel internally so None is a valid cached value.

Cache key

Arguments are converted via safe_jsonable_encoder (recursive JSON-friendly conversion handling sets, bytes, dates, Path, BaseModel, and objects with __dict__), then hashed with the function source + name. Re-decorating the same function body → same key; changing the function body → new key (transparent cache invalidation on code change).

Custom or strict encoder (default=)

safe_jsonable_encoder mirrors json.dumps(default=): pass a callable that handles types no built-in handler matched, or None for strict mode that raises on unknown types.

# strict mode — raise on anything we can't serialise
@cached(cache, default=None)
def f(x: dict) -> str:
    ...

# custom fallback — e.g. include a deterministic hash for opaque objects
def my_default(obj):
    return obj.cache_key() if hasattr(obj, "cache_key") else hashlib.md5(repr(obj).encode()).hexdigest()

@cached(cache, default=my_default)
def g(complicated_input) -> dict:
    ...

The package default is default=str, which preserves the loose 0.1 behaviour of falling back to str(obj). Use strict mode when your inputs include objects without __dict__ whose str(obj) includes a memory address — those addresses change every process invocation and would silently bust the cache key.

Cache identity & migration

Every @cached function has a stable identity — "name:body_hash" — which combines with the per-call argument hash to form the cache key. cache_id() returns it, and func.__emboss__ carries the full metadata:

from emboss import cache_id

@cached(cache)
def fetch_user(uid: int) -> dict:
    ...

cache_id(fetch_user)   # "fetch_user:3f2a9c..." (32-hex hash of the AST-canonical source)

The emboss id CLI prints the same token without writing a script — handy for capturing an identity before (or after) an edit:

emboss id mypkg.users:fetch_user            # dotted module path
emboss id src/mypkg/users.py:fetch_user     # file path
emboss id --rev HEAD~1 src/mypkg/users.py:fetch_user   # identity as of a git revision

--rev reads the file out of git (git show), so you can recover the pre-edit identity even if you forgot to capture it first. It imports the module to do this, so it's best-effort: if the module's imports fail, it reports the error and exits 1.

also_accept — keep warm entries through a rename or refactor

Renaming a function or editing its body changes its identity, so existing entries stop matching. When the behaviour is unchanged, pass the old identity and emboss falls back to the old keys on a miss — copying each hit forward to the new key (write-through), so the fallback can be dropped once the cache has migrated:

old_id = cache_id(fetch_user)   # capture before the rename, e.g. "fetch_user:3f2a9c..."

@cached(cache, also_accept=["fetch_user:3f2a9c..."])
def get_user(uid: int) -> dict:
    ...  # same behaviour, new name — old entries are reused, not recomputed

Different arguments still miss as usual — migration only redirects keys, never serves a value computed for other inputs. Malformed tokens (anything not "name:body_hash") raise ValueError at decoration time.

unsafe_manual_key — opt out of source-based invalidation

unsafe_manual_key pins the identity to a fixed string instead of the source hash:

@cached(cache, unsafe_manual_key="v1")
def summarise(text: str) -> str:
    ...  # edit freely — entries keyed on "summarise:v1" keep matching

Warning: this disables emboss's invalidate-on-edit safety net. Editing the body no longer invalidates the cache, so stale results are served until you bump the key ("v1" → "v2"). Use it only when you accept that responsibility — e.g. a hot cache you must not re-bill for cosmetic-but-not-quite-canonical churn. also_accept works alongside it, e.g. to migrate source-keyed entries into a manual-key identity.

Pluggable backends (Cache protocol)

cached accepts any object satisfying the runtime-checkable Cache protocol:

from typing import Any, Protocol, runtime_checkable

@runtime_checkable
class Cache(Protocol):
    def get(self, key: str, default: Any = None) -> Any: ...
    def set(self, key: str, value: Any) -> Any: ...

Structural typing — no inheritance required. diskcache.Cache, emboss.FileCache, and any custom Redis / in-memory adapter you write all work out of the box.

FileCache backend — NFS-safe alternative to diskcache

from emboss import FileCache, cached

cache = FileCache(".data/cache")

@cached(cache)
def expensive(x: int) -> dict:
    ...

diskcache stores entries in SQLite, and SQLite over NFS has broken file-locking — two cluster nodes hitting the same .data/cache mount on VAST get sqlite3.OperationalError: locking protocol. FileCache writes one file per key via tempfile + os.replace (atomic rename, NFS-safe), with (key, value) pickled. Concurrent writers race on the same file path but POSIX rename is atomic and the winning version is by construction equally correct (cache values are pure functions of the key).

Drop-in for the subset of diskcache.Cache API @cached uses (get, set, __contains__, __getitem__, __setitem__, __delitem__, delete, clear, close, context-manager). Extra diskcache kwargs (timeout, size_limit, eviction_policy) are accepted and ignored so call sites switch with no code changes.

Async support

@cached(cache)
async def fetch_async(url: str) -> dict:
    async with httpx.AsyncClient() as c:
        return (await c.get(url)).json()

Cache hits return a fresh awaitable wrapping the cached value, so the call site keeps await-ing as normal.

Daily-rolling caches

The diskcache.Cache instance you pass is yours to manage. A common pattern for "expire daily" without thinking about it:

from datetime import date
import diskcache
cache = diskcache.Cache(f"/tmp/my-cache-{date.today()}")

Each new day → new dir → effectively fresh cache. Old dirs land in /tmp and get reaped by the OS.

License

MIT.