# persistent-cache-decorator
[](https://pypi.org/project/persistent-cache-decorator)
[](https://pypi.org/project/persistent-cache-decorator)
[](https://results.pre-commit.ci/latest/github/FlavioAmurrioCS/persistent-cache-decorator/main)
-----
**Table of Contents**
- [persistent-cache-decorator](#persistent-cache-decorator)
- [Installation](#installation)
- [Usage](#usage)
- [Cached Property](#cached-property)
- [Creating a custom cache backend](#creating-a-custom-cache-backend)
- [License](#license)
## Installation
```console
pip install persistent-cache-decorator
```
## Usage
```python
from __future__ import annotations
import time
from persistent_cache.backend import CacheBackend
from persistent_cache.decorators import json_cache
from persistent_cache.decorators import persistent_cache
@persistent_cache(minutes=4)
def long_func(n: int) -> str:
"""Long Func Documentation."""
# Long function
time.sleep(n)
return f"{n}"
# reveal_type(long_func)
# Runtime type is "_persistent_cache"
# <persistent_cache_decorator._persistent_cache object at 0x10468be50>
# Call function(takes 5 seconds)
long_func(5)
"5"
# Call function again (takes 0 seconds)
long_func(5)
"5"
# Bypass caching(takes 5 seconds)
long_func.no_cache_call(5)
"5"
# Call function again (takes 0 seconds)
long_func(5)
"5"
# Clear cache for this function
long_func.cache_clear()
# Call function(takes 5 seconds)
long_func(5)
```
## Cached Property
```python
from typing import NamedTuple # noqa: E402
from persistent_cache.decorators import json_cached_property # noqa: E402
# To cache instance methods, use the json_cache decorator you can do the following:
# Reference: https://www.youtube.com/watch?v=sVjtp6tGo0g
class Pet:
def __init__(self, name: str, age: int) -> None:
self.name = name
self.age = age
# creating the cache function this way will allow the cache to be cleared using the instance
# It will also only use the arguments as the key
self.online_information = json_cache(days=2)(self._online_information)
def _online_information(self, source: str) -> int:
# Something that takes a long time
return len(source)
pet = Pet("Rex", 5)
pet.online_information(source="https://api.github.com/users/rex")
pet.online_information.cache_clear()
# NEW: or you can use the json_cached_property decorator to cache the result of a method
# This makes use of Python's Descriptors: https://www.youtube.com/watch?v=vBys0SwYvCQ
class Person(NamedTuple):
name: str
age: int
# The decorator works with Namedtuples as well as with classes
@json_cached_property(days=2)
def online_information(self, source: str) -> int:
# Something that takes a long time
return len(source)
person = Person("John", 30)
# The following call will cache the result using the class instance as well as the arguments as the key # noqa: E501
person.online_information(source="https://api.github.com/users/john")
# To clear the cache, use the method from the class directly
Person.online_information.cache_clear()
```
## Creating a custom cache backend
```python
from typing_extensions import Any # noqa: E402
from typing import Callable # noqa: E402
from persistent_cache.decorators import cache_decorator_factory # noqa: E402
from typing import TYPE_CHECKING # noqa: E402
if TYPE_CHECKING:
import datetime
from persistent_cache.decorators import _R
# Define a custom cache backend
class RedisCacheBackend(CacheBackend):
def get_cache_or_call( # type: ignore[empty-body]
self,
*,
func: Callable[..., _R],
args: tuple[Any, ...],
kwargs: dict[str, Any],
lifespan: datetime.timedelta,
) -> _R:
...
def del_func_cache(self, *, func: Callable[..., Any]) -> None:
...
# Singleton Instance
REDIS_CACHE_BACKEND = RedisCacheBackend()
# Quick way of defining a decorator. You can use this if you want multiple decorators with different cache durations. # noqa: E501
redis_cache = cache_decorator_factory(backend=REDIS_CACHE_BACKEND)
@redis_cache(days=1)
def foo(time: float) -> float:
from time import sleep
sleep(time)
return time
```
## License
`persistent-cache-decorator` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
Raw data
{
"_id": null,
"home_page": null,
"name": "persistent-cache-decorator",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "JSON, SQLite, cache, decorator, persistence, pickle",
"author": null,
"author_email": "Flavio Amurrio <25621374+FlavioAmurrioCS@users.noreply.github.com>",
"download_url": "https://files.pythonhosted.org/packages/91/1b/46398de5d7800c231e49a0c492b569b879b705a6be78706af029b64012a1/persistent_cache_decorator-0.1.10.tar.gz",
"platform": null,
"description": "# persistent-cache-decorator\n\n[](https://pypi.org/project/persistent-cache-decorator)\n[](https://pypi.org/project/persistent-cache-decorator)\n[](https://results.pre-commit.ci/latest/github/FlavioAmurrioCS/persistent-cache-decorator/main)\n\n-----\n\n**Table of Contents**\n\n- [persistent-cache-decorator](#persistent-cache-decorator)\n - [Installation](#installation)\n - [Usage](#usage)\n - [Cached Property](#cached-property)\n - [Creating a custom cache backend](#creating-a-custom-cache-backend)\n - [License](#license)\n\n## Installation\n\n```console\npip install persistent-cache-decorator\n```\n\n## Usage\n```python\nfrom __future__ import annotations\n\nimport time\n\nfrom persistent_cache.backend import CacheBackend\nfrom persistent_cache.decorators import json_cache\nfrom persistent_cache.decorators import persistent_cache\n\n\n@persistent_cache(minutes=4)\ndef long_func(n: int) -> str:\n \"\"\"Long Func Documentation.\"\"\"\n # Long function\n time.sleep(n)\n return f\"{n}\"\n\n\n# reveal_type(long_func)\n# Runtime type is \"_persistent_cache\"\n# <persistent_cache_decorator._persistent_cache object at 0x10468be50>\n\n# Call function(takes 5 seconds)\nlong_func(5)\n\"5\"\n# Call function again (takes 0 seconds)\nlong_func(5)\n\"5\"\n\n# Bypass caching(takes 5 seconds)\nlong_func.no_cache_call(5)\n\"5\"\n\n# Call function again (takes 0 seconds)\nlong_func(5)\n\"5\"\n# Clear cache for this function\nlong_func.cache_clear()\n\n# Call function(takes 5 seconds)\nlong_func(5)\n```\n\n## Cached Property\n```python\nfrom typing import NamedTuple # noqa: E402\nfrom persistent_cache.decorators import json_cached_property # noqa: E402\n\n\n# To cache instance methods, use the json_cache decorator you can do the following:\n# Reference: https://www.youtube.com/watch?v=sVjtp6tGo0g\nclass Pet:\n def __init__(self, name: str, age: int) -> None:\n self.name = name\n self.age = age\n # creating the cache function this way will allow the cache to be cleared using the instance\n # It will also only use the arguments as the key\n self.online_information = json_cache(days=2)(self._online_information)\n\n def _online_information(self, source: str) -> int:\n # Something that takes a long time\n return len(source)\n\n\npet = Pet(\"Rex\", 5)\npet.online_information(source=\"https://api.github.com/users/rex\")\npet.online_information.cache_clear()\n\n\n# NEW: or you can use the json_cached_property decorator to cache the result of a method\n# This makes use of Python's Descriptors: https://www.youtube.com/watch?v=vBys0SwYvCQ\nclass Person(NamedTuple):\n name: str\n age: int\n\n # The decorator works with Namedtuples as well as with classes\n @json_cached_property(days=2)\n def online_information(self, source: str) -> int:\n # Something that takes a long time\n return len(source)\n\n\nperson = Person(\"John\", 30)\n\n# The following call will cache the result using the class instance as well as the arguments as the key # noqa: E501\nperson.online_information(source=\"https://api.github.com/users/john\")\n\n# To clear the cache, use the method from the class directly\nPerson.online_information.cache_clear()\n```\n\n## Creating a custom cache backend\n\n```python\nfrom typing_extensions import Any # noqa: E402\nfrom typing import Callable # noqa: E402\nfrom persistent_cache.decorators import cache_decorator_factory # noqa: E402\nfrom typing import TYPE_CHECKING # noqa: E402\n\nif TYPE_CHECKING:\n import datetime\n from persistent_cache.decorators import _R\n\n\n# Define a custom cache backend\nclass RedisCacheBackend(CacheBackend):\n def get_cache_or_call( # type: ignore[empty-body]\n self,\n *,\n func: Callable[..., _R],\n args: tuple[Any, ...],\n kwargs: dict[str, Any],\n lifespan: datetime.timedelta,\n ) -> _R:\n ...\n\n def del_func_cache(self, *, func: Callable[..., Any]) -> None:\n ...\n\n\n# Singleton Instance\nREDIS_CACHE_BACKEND = RedisCacheBackend()\n\n# Quick way of defining a decorator. You can use this if you want multiple decorators with different cache durations. # noqa: E501\nredis_cache = cache_decorator_factory(backend=REDIS_CACHE_BACKEND)\n\n\n@redis_cache(days=1)\ndef foo(time: float) -> float:\n from time import sleep\n\n sleep(time)\n return time\n```\n\n## License\n\n`persistent-cache-decorator` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.\n",
"bugtrack_url": null,
"license": null,
"summary": "A decorator for caching functions that provides persistence to JSON, pickle, or SQLite",
"version": "0.1.10",
"project_urls": {
"Documentation": "https://github.com/FlavioAmurrioCS/persistent-cache-decorator#readme",
"Issues": "https://github.com/FlavioAmurrioCS/persistent-cache-decorator/issues",
"Source": "https://github.com/FlavioAmurrioCS/persistent-cache-decorator"
},
"split_keywords": [
"json",
" sqlite",
" cache",
" decorator",
" persistence",
" pickle"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "fe8ef4704df4c808dfb8d6710ff4f3329031c2f5f7f19e97e93bd74ea33d7998",
"md5": "7efbc4614b03ca4a2507bc7af54c06d0",
"sha256": "6da2b795bf8fe7d5b525647e412a67e57be66c79b1c1f4f2c6f5f2ff5011f025"
},
"downloads": -1,
"filename": "persistent_cache_decorator-0.1.10-py3-none-any.whl",
"has_sig": false,
"md5_digest": "7efbc4614b03ca4a2507bc7af54c06d0",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 14366,
"upload_time": "2025-08-02T07:58:20",
"upload_time_iso_8601": "2025-08-02T07:58:20.942995Z",
"url": "https://files.pythonhosted.org/packages/fe/8e/f4704df4c808dfb8d6710ff4f3329031c2f5f7f19e97e93bd74ea33d7998/persistent_cache_decorator-0.1.10-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "911b46398de5d7800c231e49a0c492b569b879b705a6be78706af029b64012a1",
"md5": "9dd22c594ecae4436ddb5ed947971bf8",
"sha256": "e3df3a6e80c671fa83f835512c4fc5cc0c34c96b13f682b44075836964ecb91e"
},
"downloads": -1,
"filename": "persistent_cache_decorator-0.1.10.tar.gz",
"has_sig": false,
"md5_digest": "9dd22c594ecae4436ddb5ed947971bf8",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 17347,
"upload_time": "2025-08-02T07:58:24",
"upload_time_iso_8601": "2025-08-02T07:58:24.548416Z",
"url": "https://files.pythonhosted.org/packages/91/1b/46398de5d7800c231e49a0c492b569b879b705a6be78706af029b64012a1/persistent_cache_decorator-0.1.10.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-02 07:58:24",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "FlavioAmurrioCS",
"github_project": "persistent-cache-decorator#readme",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "persistent-cache-decorator"
}
JSON
