memoization 0.2.2

A powerful caching library for Python, with TTL support and multiple algorithm options. (https://github.com/lonelyenvoy/python-memoization)

python-memoization

A powerful caching library for Python, with TTL support and multiple algorithm options.

If you like this work, please star it on GitHub.

Why choose this library?

Perhaps you know about functools.lru_cache in Python 3, and you may be wondering why I am reinventing the wheel.

Well, actually not. This lib is based on functools. Please find below the comparison with lru_cache.

Features	functools.lru_cache	memoization
Configurable max size	✔️	✔️
Thread safety	✔️	✔️
Flexible argument typing (typed & untyped)	✔️	Always typed
Cache statistics	✔️	✔️
LRU (Least Recently Used) as caching algorithm	✔️	✔️
LFU (Least Frequently Used) as caching algorithm	No support	✔️
FIFO (First In First Out) as caching algorithm	No support	✔️
Extensibility for new caching algorithms	No support	✔️
TTL (Time-To-Live) support	No support	✔️
Support for unhashable arguments (dict, list, etc.)	No support	✔️
Partial cache clearing	No support	Pending implementation in v0.3.x
Python version	3.2+	3.4+

memoization solves some drawbacks of functools.lru_cache:

1. lru_cache does not support unhashable types, which means function arguments cannot contain dict or list.

>>> from functools import lru_cache
>>> @lru_cache()
... def f(x): return x
... 
>>> f([1, 2])  # unsupported
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'list'

2. lru_cache is vulnerable to hash collision attack and can be hacked or compromised. In memoization, caching is always typed, which means f(3) and f(3.0) will be treated as different calls and cached separately. This prevents the attack from happening (or at least makes it a lot harder).

>>> hash((1,))
3430019387558
>>> hash(3430019387558.0)  # two different arguments have an identical hash value
3430019387558

3. Unlike lru_cache, memoization is designed to be highly extensible, which make it easy for developers to add and integrate any caching algorithms (beyond FIFO, LRU and LFU) into this library. See Contributing Guidance for further detail.

Installation

pip install memoization

Usage in 2 lines

from memoization import cached

@cached
def func(arg):
    ...  # do something slow

Simple enough - the results of func() are cached. Repetitive calls to func() with the same arguments run func() only once, enhancing performance.

Advanced features

Configurable options include ttl, max_size, algorithm and thread_safe.

TTL (Time-To-Live)

@cached(ttl=5)  # the cache expires after 5 seconds
def expensive_db_query(user_id):
    ...

For impure functions, TTL (in second) will be a solution. This will be useful when the function returns resources that is valid only for a short time, e.g. fetching something from databases.

Limited cache capacity

@cached(max_size=128)  # the cache holds no more than 128 items
def get_a_very_large_object(filename):
    ...

By default, if you don't specify max_size, the cache can hold unlimited number of items. When the cache is fully occupied, the former data will be overwritten by a certain algorithm described below.

Choosing your caching algorithm

from memoization import cached, CachingAlgorithmFlag

@cached(max_size=128, algorithm=CachingAlgorithmFlag.LFU)  # the cache overwrites items using the LFU algorithm
def func(arg):
    ...

Possible values for algorithm are:

• CachingAlgorithmFlag.LRU: Least Recently Used (default)
• CachingAlgorithmFlag.LFU: Least Frequently Used
• CachingAlgorithmFlag.FIFO: First In First Out

This option is valid only when a max_size is explicitly specified.

Thread safe?

@cached(thread_safe=False)
def func(arg):
    ...

thread_safe is True by default. Setting it to False enhances performance.

Knowing how well the cache is behaving

>>> @cached
... def f(x): return x
... 
>>> f.cache_info()
CacheInfo(hits=0, misses=0, current_size=0, max_size=None, algorithm=<CachingAlgorithmFlag.LRU: 2>, ttl=None, thread_safe=True)

With cache_info, you can retrieve the number of hits and misses of the cache, and other information indicating the caching status.

• hits: the number of cache hits
• misses: the number of cache misses
• current_size: the number of items that were cached
• max_size: the maximum number of items that can be cached (user-specified)
• algorithm: caching algorithm (user-specified)
• ttl: Time-To-Live value (user-specified)
• thread_safe: whether the cache is thread safe (user-specified)

Other APIs

• Access the original function f by f.__wrapped__.
• Clear the cache by f.cache_clear().

Contributing

This project welcomes contributions from anyone.

• Read Contributing Guidance first.
• Submit bugs and help me verify fixes.
• Submit pull requests for bug fixes and features and discuss existing proposals. Please make sure that your PR passes the tests in test.py.
• See contributors of this project.

License

The MIT License