Extensible memoizing collections and decorators
Project description
This module provides various memoizing collections and decorators, including variants of the Python 3 Standard Library @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
>>> cache
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. by adding another item the cache would exceed 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 total size of its items, and an item’s size is a property or function of its value, e.g. the result of sys.getsizeof(value). For the trivial but common case that each item counts as 1, a cache’s size is equal to the number of its items, or len(cache).
The cachetools module implements multiple cache classes based on different caching algorithms, as well as decorators for easily memoizing function and method calls.
Installation
Install cachetools using pip:
pip install cachetools
Project Resources
License
Copyright (c) 2014, 2015 Thomas Kemmer.
Licensed under the MIT License.
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