| Name | tempcache JSON |
| Version |
0.0.11
JSON |
| download |
| home_page | None |
| Summary | Python library to cache data and function results in temporary files |
| upload_time | 2025-02-27 20:12:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
caching
tempfiles
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Python library to cache data and function results in temporary files
This library offers a simple way to cache data and function results using temporary files, including a mechanism for automatic expiration after a certain time.
Dy default, the package uses the `pickle` module to serialize data and hash key values.
> **Note**
For more advanced use cases you may want to look at the `Memory` class
in [joblib](https://github.com/joblib/joblib).
## Basic Usage
An instance of the `TempCache` class be used as a decorator
to automatically cache the results of a function.
```python
from tempcache import TempCache
CACHE_MAX_AGE = 24 * 60 * 60 * 2 # two days
cache = TempCache("mycache", max_age=CACHE_MAX_AGE)
@cache
def long_running(...):
...
result = long_running(...)
```
## Caching results at the call site
You can also use a `TempCache` object to cache a result
at the call site with the `cache_result` method.
```python
from tempcache import TempCache
CACHE_MAX_AGE = 24 * 60 * 60 * 2 # two days
cache = TempCache("mycache", max_age=CACHE_MAX_AGE)
def long_running(...):
...
result = cache.cache_result(long_running, ...)
```
## Advanced usage
In cases where the function or some of its arguments
are defined in the `__main__` namespace or in a jupyter notebook
and cannot be pickled by `pickle` you can use a different pickle module
like `cloupickle`.
```python
import cloudpickle
from tempcache import TempCache
CACHE_MAX_AGE = 24 * 60 * 60 * 2 # two days
cache = TempCache("mycache",
pickler=cloudpickle,
max_age=CACHE_MAX_AGE)
key = ...
# key object can be complex as long as it is pickle-able
item = cache.item_for_key(key)
# cache item for the given key whether it exists or not
# load item if it exists
if item.exists():
value = item.load()
# save item
item.save(value)
```
## Examples
Examples notebooks are in the `extras` folder.
## Installation
You can install this package with `pip`.
```console
pip install tempcache
```
## Related Projects
- [joblib](https://github.com/joblib/joblib)
Computing with Python functions
- [percache](https://pypi.org/project/percache/)
Persistently cache results of callables
- [disckcache](https://pypi.org/project/diskcache/)
Disk and file backed cache library compatible with Django
- [cloudpickle](https://github.com/cloudpipe/cloudpickle)
Extended pickling support for Python objects
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"description": "# Python library to cache data and function results in temporary files\n\nThis library offers a simple way to cache data and function results using temporary files, including a mechanism for automatic expiration after a certain time.\n\nDy default, the package uses the `pickle` module to serialize data and hash key values.\n\n\n> **Note**\nFor more advanced use cases you may want to look at the `Memory` class\nin [joblib](https://github.com/joblib/joblib).\n\n\n## Basic Usage\n\nAn instance of the `TempCache` class be used as a decorator\nto automatically cache the results of a function.\n\n```python\nfrom tempcache import TempCache\n\nCACHE_MAX_AGE = 24 * 60 * 60 * 2 # two days\ncache = TempCache(\"mycache\", max_age=CACHE_MAX_AGE)\n\n@cache\ndef long_running(...):\n ...\n\nresult = long_running(...)\n```\n\n## Caching results at the call site\n\nYou can also use a `TempCache` object to cache a result\nat the call site with the `cache_result` method. \n\n```python\nfrom tempcache import TempCache\n\nCACHE_MAX_AGE = 24 * 60 * 60 * 2 # two days\ncache = TempCache(\"mycache\", max_age=CACHE_MAX_AGE)\n\ndef long_running(...):\n ...\n\nresult = cache.cache_result(long_running, ...)\n```\n\n## Advanced usage\n\nIn cases where the function or some of its arguments\nare defined in the `__main__` namespace or in a jupyter notebook\nand cannot be pickled by `pickle` you can use a different pickle module\nlike `cloupickle`.\n\n\n```python\nimport cloudpickle\n\nfrom tempcache import TempCache\n\nCACHE_MAX_AGE = 24 * 60 * 60 * 2 # two days\ncache = TempCache(\"mycache\",\n pickler=cloudpickle,\n max_age=CACHE_MAX_AGE)\n\nkey = ...\n# key object can be complex as long as it is pickle-able\n\nitem = cache.item_for_key(key)\n# cache item for the given key whether it exists or not\n\n# load item if it exists\nif item.exists():\n value = item.load()\n\n# save item\nitem.save(value)\n```\n\n\n## Examples\n\nExamples notebooks are in the `extras` folder.\n\n## Installation\n\nYou can install this package with `pip`.\n\n```console\npip install tempcache\n```\n\n## Related Projects\n\n- [joblib](https://github.com/joblib/joblib)\nComputing with Python functions\n- [percache](https://pypi.org/project/percache/)\nPersistently cache results of callables\n- [disckcache](https://pypi.org/project/diskcache/)\nDisk and file backed cache library compatible with Django\n- [cloudpickle](https://github.com/cloudpipe/cloudpickle)\nExtended pickling support for Python objects\n",
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