cachier 0.1.25

Persistent, stale-free memoization decorators for Python.

Persistent, stale-free cache / memoization decorators for Python.

from cachier import cachier
import datetime

SHELF_LIFE = datetime.timedelta(days=3)

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

Dependencies and Setup

Cachier uses the following packages:

• pymongo

• watchdog

You can install cachier using:

pip install cachier

Features

• 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).

Future features:

• S3 core.

• Multi-core caching.

Use

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.

Setting up a Cache

You can add a deafult, 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}

Resetting a Cache

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()

Setting Shelf Live

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 usefull for lengthy calculations that depend on a dynamic data source.

Fuzzy Shelf Live

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.

Cachier Cores

Pickle Core

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

You can slightly optimize 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 nullfying 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 unecessary function calls).

MongoDB Core

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

@cachier(mongetter=False)

This allows you to have a cross-machine, albeit slower, cache.