Extensible memoizing collections and decorators
This module provides various memoizing collections and decorators, including a variant of the Python 3 Standard Library functools.lru_cache function decorator.
>>> from cachetools import LRUCache >>> cache = LRUCache(maxsize=2) >>> cache.update([('first', 1), ('second', 2)]) >>> cache LRUCache([('second', 2), ('first', 1)], maxsize=2, currsize=2) >>> cache['third'] = 3 >>> cache LRUCache([('second', 2), ('third', 3)], maxsize=2, currsize=2) >>> cache['second'] 2 >>> cache['fourth'] = 4 LRUCache([('second', 2), ('fourth', 4)], maxsize=2, currsize=2)
For the purpose of this module, a cache is a mutable mapping of a fixed maximum size. When the cache is full, i.e. the current size of the cache exceeds its maximum size, the cache must choose which item(s) to discard based on a suitable cache algorithm.
In general, a cache’s size is the sum of the size of its items. If the size of each items is 1, a cache’s size is equal to the number of its items, i.e. len(cache). An items’s size may also be a property or function of its value, e.g. the result of sys.getsizeof(), or len() for string and sequence values.
This module provides various cache implementations based on different cache algorithms, as well as decorators for easily memoizing function and method calls.
Install cachetools using pip:
pip install cachetools
Copyright (c) 2014 Thomas Kemmer.
Licensed under the MIT License.