OneCache
Python cache for sync and async code.
Cache uses an LRU algorithm and can optionally set TTLs per entry.
On CPython the cache core is a compiled Cython extension (prebuilt wheels for Linux, macOS and Windows on x86_64 and ARM64). On PyPy, or on platforms without a prebuilt wheel, it falls back to an equivalent pure Python implementation. onecache.COMPILED tells which one is in use.
Tested automatically on CPython 3.10 and 3.14 and PyPy 3.11 across Linux, macOS, and Windows on x86_64 and ARM64 (see the workflow badge). Python 3.8 and 3.9 are still installable and get prebuilt wheels, but are no longer part of the test matrix.
Usage
from onecache import CacheDecorator
from onecache import AsyncCacheDecorator
class Counter:
def __init__(self, count=0):
self.count = count
@pytest.mark.asyncio
async def test_async_cache_counter():
"""Test async cache, counter case."""
counter = Counter()
@AsyncCacheDecorator()
async def mycoro(counter: Counter):
counter.count += 1
return counter.count
assert 1 == (await mycoro(counter))
assert 1 == (await mycoro(counter))
def test_cache_counter():
"""Test async cache, counter case."""
counter = Counter()
@CacheDecorator()
def sample(counter: Counter):
counter.count += 1
return counter.count
assert 1 == (sample(counter))
assert 1 == (sample(counter))
Decorator classes supports the following arguments
- maxsize (int): Maximun number of items to be cached. default: 512
- ttl (int): time to expire in milliseconds, if None, it does not expire. default: None
- skip_args (bool): apply cache as the function doesn't have any arguments, default: False
- cache_class (class): Class to use for cache instance. default: LRUCache
- refresh_ttl (bool): if cache with ttl, This flag makes key expiration timestamp to be refresh per access. default: False
- thread_safe (bool): tell decorator to use thread safe lock. default=False
- max_mem_size (int): max mem size in bytes, as the sum over every entry of the shallow
sys.getsizeofof its value, its key and, when the key is a tuple of arguments, each of those arguments. default=None which means no limit. For pypy this value is ignored as the objects can change by the JIT compilation.
If num of records exceds maxsize, it drops the least recently used one. With max_mem_size, a value bigger than the limit is not cached.
Use thread_safe=True when the decorated function is called from several threads.
Arguments are keyed like functools.lru_cache (with typed=False) does:
- A single
intorstrargument is the key itself; any other call is keyed by the tuple of its arguments and keyword arguments (keyword order doesn't matter). Arguments are compared by hash and equality, sof(1, 2),f(1.0, 2)andf(True, 2)share an entry, whilef(1)andf("1")don't. - If any argument isn't hashable (a list, dict, set, a subclass of them, a non-frozen dataclass, or a tuple holding one of those), the whole call is keyed by the string form of its arguments, like onecache did before 0.10.0. Those string keys can collide, for example
f([1, 2])andf(["1,2"])share an entry, and an object without a custom__str__is keyed by its address, which a later object can reuse once it is garbage collected. - With
thread_safe=True, the arguments'__hash__and__eq__run while the cache lock is held, so keep them cheap and don't make them wait on other threads. - The key holds a reference to the arguments, so the cache keeps them alive (including
selffor decorated methods) until the entry is evicted or expires. Usettl, a smallermaxsizeor.cache.clear()if that matters. - Arguments should keep the same hash while they are cached. If one changes, or its
__hash__starts raising, later calls with it miss the cache, and evicting or expiring its old entry may clear the whole cache.
To key every call by strings instead, use a cache_class subclass that overrides the serialize_key classmethod (subclasses don't use the compiled fast path).
For a function whose result depends on the exact argument type (for example bool vs int, or float vs Decimal), use a cache_class subclass that overrides serialize_key.
Upgrading to 0.10.0: cached results are now shared by calls whose arguments compare equal (f(1, 2) and f(1.0, 2), or f(1.0) and f(True); a lone int argument stays separate, so f(1) and f(1.0) don't share), objects are keyed by __hash__/__eq__ instead of __str__, and code that reads or expires entries by hand must build keys with .cache.get_key(*args, **kwargs) instead of strings such as "1".
The decorated function exposes its cache as .cache: .cache.expire(*args, **kwargs) drops one entry, .cache.clear() drops all of them, and .cache.cache is a read-only view of the entries.
Set ONECACHE_NO_EXTENSIONS to any non-empty value to force the pure Python implementation. If the extension failed to build when installing from source (e.g. no compiler), pip may keep reusing that pure Python build from its cache; reinstall with pip install --no-cache-dir --force-reinstall onecache after installing a compiler.
Performance
Compared with functools.lru_cache (CPython's C implementation), in nanoseconds per call (lower is better), measured with benchmarks/compare_lru_cache.py on CPython 3.12, macOS x86_64, maxsize=512:
| Case | lru_cache | onecache (Cython) | Cython vs lru_cache | onecache (pure Python) |
|---|---|---|---|---|
| Hit, 1 positional arg | 88 | 138 | 1.6x slower | 767 |
| Hit, 2 args + 1 kwarg | 234 | 488 | 2.1x slower | 1,592 |
| Miss on a full cache (evicts) | 252 | 462 | 1.8x slower | 1,901 |
| Hit, thread safe¹ | 88 | 180 | 2.1x slower | 788 |
| Hit, with ttl | n/a | 225 | n/a | 1,211 |
| Hit, list argument | n/a | 580 | n/a | 2,239 |
lru_cache is still faster: it is built into CPython, while onecache's decorated function goes through a Cython call wrapper and does more bookkeeping per call. In exchange, onecache supports TTLs, async functions, expiring a single entry with expire(), a memory limit, and unhashable arguments such as lists and dicts (lru_cache raises TypeError for them). If you only need a plain LRU cache for hashable arguments, functools.lru_cache is the faster choice.
¹ lru_cache has no lock option, so its thread safe row is the same call as the first row.
The pure Python column is what PyPy and platforms without a prebuilt wheel use. Numbers depend on the machine; run python benchmarks/compare_lru_cache.py (after make build_ext) to measure yours.
Development
Install dependencies with Poetry (includes dev + test groups):
poetry install --with test,dev
Build the Cython extension in place (needed after any change to onecache/_ccore.pyx):
make build_ext
Run the test suite against the compiled and the pure Python implementations:
poetry run pytest
ONECACHE_NO_EXTENSIONS=1 poetry run pytest
onecache/_ccore.pyx and onecache/_core.py implement the same API and must be kept in sync.
Releasing
Pull requests only build cp310 and cp314 wheels, so first run the Wheels workflow by hand on master (Actions → Wheels → Run workflow) to build and test every Python version. Then bump version in pyproject.toml and push a tag with the same value (e.g. 0.9.0). The Wheels workflow builds the wheels with cibuildwheel, the sdist and a pure Python fallback wheel, and publishes them to PyPI through trusted publishing. That needs a pypi environment in the GitHub repository settings and a trusted publisher on PyPI for this repository, workflow wheels.yml and environment pypi.
Lint and format checks:
poetry run flake8
poetry run autopep8 --in-place --recursive onecache tests
Contribute
- Fork
- create a branch
feature/your_feature - commit - push - pull request
Thanks :)
1 maintainer
Metadata
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