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Minimal persistent memoization cache

Project description

Cachalot PyPI version Pipeline status Coverage reportDownloads

Cachalot is a minimal persistent memoization cache. It provides a decorator, that stores function result for future use. Perfect for heavy computations and I/O operation (such as web requests). On backend, it uses TinyDB for storage.


  • Simple usage via decorator
  • Persistent caching
  • Key expiration
  • Maximum cache size, to prevent bloat


Cachalot requires Python 3.5 or newer to run.

Python package

You can easily install Cachalot using pip:

pip3 install cachalot


Alternatively, to get the latest development version, you can clone this repository and then manually install it:

git clone
cd cachalot
python3 install


from cachalot import Cache

def expensive_function():
    return expensive_calculation()

Advanced usage

from cachalot import Cache

@Cache(path='cache.json', timeout=3600, size=5e3, filesize=1e6, retry=True, renew_on_read=True)
def expensive_function():
    return expensive_calculation()
  • path: Path to the database file. Defaults to .cache.json.
  • timeout: How long should the data be cached in seconds. Defaults to 0 (infinite).
  • size: Maximum number of keys cached. Defaults to 0 (infinite).
  • filesize: Maximum size of database file in bytes. Defaults to 0 (infinite).
  • retry: Retry if result is blank. Defaults to False.
  • renew_on_read: Renew the entry, i.e refresh the entry timestamp on reads. Defaults to True

Manually deleting entries

If you want to manually invalidate an entry, you can calculate the hash of the function call and then pass it the remove method.

key = cache.calculate_hash(len)('teststring')

For more information, see documentation.


For information on how to contribute to the project, please check the Contributor's Guide




MIT License


This package was created with Cookiecutter and the python-cookiecutter project template. Inspired by Cashier

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