Persistent, stale-free, local and cross-machine caching for Python functions.

from cachier import cachier
import datetime

@cachier(stale_after=datetime.timedelta(days=3))
def foo(arg1, arg2):
  """foo now has a persistent cache, trigerring recalculation for values stored more than 3 days."""
  return {'arg1': arg1, 'arg2': arg2}

Install cachier with:

pip install cachier

For the latest version supporting Python 2.7 please use:

pip install 'cachier==1.2.8'

• Pure Python.
• Compatible with Python 3.5 (and Python 2.7 up until version 1.2.8).
• Supported and tested on Linux, OS X and Windows.
• A simple interface.
• Defining "shelf life" for cached values.
• Local caching using pickle files.
• Cross-machine caching using MongoDB.
• Thread-safety.

Cachier is NOT:

• Meant as a transient cache. Python's @lru_cache is better.
• Especially fast. It is meant to replace function calls that take more than... a second, say (overhead is around 1 millisecond).

• S3 core.
• Multi-core caching.
• Cache replacement policies

Cachier provides a decorator which you can wrap around your functions to give them a persistent cache. The positional and keyword arguments to the wrapped function must be hashable (i.e. Python's immutable built-in objects, not mutable containers). Also, notice that since objects which are instances of user-defined classes are hashable but all compare unequal (their hash value is their id), equal objects across different sessions will not yield identical keys.

You can add a default, pickle-based, persistent cache to your function - meaning it will last across different Python kernels calling the wrapped function - by decorating it with the cachier decorator (notice the ()!).

from cachier import cachier

@cachier()
def foo(arg1, arg2):
  """Your function now has a persistent cache mapped by argument values!"""
  return {'arg1': arg1, 'arg2': arg2}

The Cachier wrapper adds a clear_cache() function to each wrapped function. To reset the cache of the wrapped function simply call this method:

foo.clear_cache()

To limit the number of threads Cachier is allowed to spawn, set the CACHIER_MAX_WORKERS with the desired number. The defeault is 8, so to enable Cachier to spawn even more threads, you'll have to set a higher limit explicitly.

You can set any duration as the shelf life of cached return values of a function by providing a corresponding timedelta object to the stale_after parameter:

import datetime

@cachier(stale_after=datetime.timedelta(weeks=2))
def bar(arg1, arg2):
  return {'arg1': arg1, 'arg2': arg2}

Now when a cached value matching the given arguments is found the time of its calculation is checked; if more than stale_after time has since passed, the function will be run again for the same arguments and the new value will be cached and returned.

This is useful for lengthy calculations that depend on a dynamic data source.

Sometimes you may want your function to trigger a calculation when it encounters a stale result, but still not wait on it if it's not that critical. In that case, you can set next_time to True to have your function trigger a recalculation in a separate thread, but return the currently cached stale value:

@cachier(next_time=True)

Further function calls made while the calculation is being performed will not trigger redundant calculations.

As mentioned above, the positional and keyword arguments to the wrapped function must be hashable (i.e. Python's immutable built-in objects, not mutable containers). To get around this limitation the hash_params parameter of the cachier decorator can be provided with a callable that gets the args and kwargs from the decorated function and returns a hash key for them.

@cachier(hash_params=hash_my_custom_class)
def calculate_super_complex_stuff(custom_obj):
  # amazing code goes here

See here for an example:

Question: How to work with unhashable arguments

Cachier also accepts several keyword arguments in the calls of the function it wraps rather than in the decorator call, allowing you to modify its behaviour for a specific function call.

You can have cachier ignore any existing cache for a specific function call by passing ignore_cache=True to the function call. The cache will neither be checked nor updated with the new return value.

@cachier()
def sum(first_num, second_num):
  return first_num   second_num

def main():
  print(sum(5, 3, ignore_cache=True))

You can have cachier overwrite an existing cache entry - if one exists - for a specific function call by passing overwrite_cache=True to the function call. The cache will not be checked but will be updated with the new return value.

You can have cachier print out a detailed explanation of the logic of a specific call by passing verbose_cache=True to the function call. This can be useful if you are not sure why a certain function result is, or is not, returned.

The default core for Cachier is pickle based, meaning each function will store its cache is a separate pickle file in the ~/.cachier directory. Naturally, this kind of cache is both machine-specific and user-specific.

You can configure cachier to use another directory by providing the cache_dir parameter with the path to that directory:

@cachier(cache_dir='~/.temp/.cache')

You can slightly optimise pickle-based caching if you know your code will only be used in a single thread environment by setting:

@cachier(pickle_reload=False)

This will prevent reading the cache file on each cache read, speeding things up a bit, while also nullifying inter-thread functionality (the code is still thread safe, but different threads will have different versions of the cache at times, and will sometime make unnecessary function calls).

Setting the optional argument separate_files to True will cause the cache to be stored in several files: A file per argument set, per function. This can help if your per-function cache files become too large.

from cachier import cachier

@cachier(separate_files=True)
def foo(arg1, arg2):
  """Your function now has a persistent cache mapped by argument values, split across several files, per argument set"""
  return {'arg1': arg1, 'arg2': arg2}

You can get the fully qualified path to the directory of cache files used by cachier (~/.cachier by default) by calling the cache_dpath() function:

>>> foo.cache_dpath()
    "/home/bigus/.cachier/"

You can set a MongoDB-based cache by assigning mongetter with a callable that returns a pymongo.Collection object with writing permissions:

  from pymongo import MongoClient

  def my_mongetter():
      client = MongoClient(get_cachier_db_auth_uri())
      db_obj = client['cachier_db']
      if 'someapp_cachier_db' not in db_obj.list_collection_names():
          db_obj.create_collection('someapp_cachier_db')
      return db_obj['someapp_cachier_db']

@cachier(mongetter=my_mongetter)

This allows you to have a cross-machine, albeit slower, cache. This functionality requires that the installation of the pymongo python package.

In certain cases the MongoDB backend might leave a deadlock behind, blocking all subsequent requests from being processed. If you encounter this issue, supply the wait_for_calc_timeout with a reasonable number of seconds; calls will then wait at most this number of seconds before triggering a recalculation.

@cachier(mongetter=False, wait_for_calc_timeout=2)

You can set an in-memory cache by assigning the backend parameter with 'memory':

@cachier(backend='memory')

Note, however, that cachier's in-memory core is simple, and has no monitoring or cap on cache size, and can thus lead to memory errors on large return values - it is mainly intended to be used with future multi-core functionality. As a rule, Python's built-in lru_cache is a much better stand-alone solution.

Package author and current maintainer is Shay Palachy ([email protected]); You are more than welcome to approach him for help. Contributions are very welcomed.

Clone:

git clone [email protected]:python-cachier/cachier.git

Install in development mode with test dependencies:

cd cachier
pip install -e ".[test]"

To run the tests, call the pytest command in the repository's root, or:

python -m pytest

To run only MongoDB core related tests, use:

pytest -m mongo

To run only memory core related tests, use:

pytest -m memory

To run all tests EXCEPT MongoDB core related tests, use:

pytest -m "not mongo"

To run all tests EXCEPT memory core AND MongoDB core related tests, use:

pytest -m "not (mongo or memory)"

This project is documented using the numpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings (in my personal opinion, of course). When documenting code you add to this project, please follow these conventions.

Additionally, if you update this README.rst file, use python setup.py checkdocs to validate it compiles.

Created by Shay Palachy ([email protected]).

Other major contributors:

• cthoyt - Base memory core implementation.
• amarczew - The hash_params kwarg.
• non-senses - The wait_for_calc_timeout kwarg.
• Elad Rapapor - Multi-file Pickle core, a.k.a separate_files (released on v1.5.3).
• John Didion - Support for pickle-based caching for cases where two identically-named methods of different classes are defined in the same module.

Notable bugfixers:

• MichaelRazum.
• Eric Ma - The iNotify bugfix (released on v1.5.3).
• Ofir - The iNotify bugfix (released on v1.5.3).