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

Project description

python-memoization

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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 ✔️
Custom cache keys 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'
  1. lru_cache is vulnerable to hash collision attack and can be hacked or compromised. Using this technique, attackers can make your program unexpectedly slow by feeding the cached function with certain cleverly designed inputs. However, in memoization, caching is always typed, which means f(3) and f(3.0) will be treated as different calls and cached separately. Also, you can build your own cache key with a unique hashing strategy. These measures 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
  1. 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 -U memoization

1-Minute Tutorial

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.

:warning:WARNING: for functions with unhashable arguments, the default setting may not enable memoization to work properly. See custom cache keys section below for details.

15-Minute Tutorial

You will learn about the advanced features in the following tutorial, which enable you to customize memoization .

Configurable options include ttl, max_size, algorithm, thread_safe, order_independent and custom_key_maker.

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.

Order-independent cache key

By default, the following function calls will be treated differently and cached twice, which means the cache misses at the second call.

func(a=1, b=1)
func(b=1, a=1)

You can avoid this behavior by passing an order_independent argument to the decorator, although it will slow down the performance a little bit.

@cached(order_independent=True)
def func(**kwargs):
    ...

Custom cache keys

Prior to memorize your function inputs and outputs (i.e. putting them into a cache), memoization needs to build a cache key using the inputs, so that the outputs can be retrieved later.

By default, memoization tries to combine all your function arguments and calculate its hash value using hash(). If it turns out that parts of your arguments are unhashable, memoization will fall back to turning them into a string using str(). This behavior relies on the assumption that the string exactly represents the internal state of the arguments, which is true for built-in types.

However, this is not true for all objects. If you pass objects which are instances of non-built-in classes, sometimes you will need to override the default key-making procedure, because the str() function on these objects may not hold the correct information about their states.

Here are some suggestions. Implementations of a valid key maker:

  • MUST be a function with the same signature as the cached function.
  • MUST produce unique keys, which means two sets of different arguments always map to two different keys.
  • MUST produce hashable keys, and a key is comparable with another key (memoization only needs to check for their equality).
  • should compute keys efficiently and produce small objects as keys.

Example:

def get_employee_id(employee):
    return employee.id

@cached(custom_key_maker=get_employee_id)
def calculate_performance(employee):
    ...

Note that writing a robust key maker function can be challenging in some situations. If you find it difficult, feel free to ask me for help by submitting an issue.

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, order_independent=False, use_custom_key=False)

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)
  • order_independent: whether the cache is kwarg-order-independent (user-specified)
  • use_custom_key: whether a custom key maker is used

Other APIs

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

Contributing

This project welcomes contributions from anyone.

License

The MIT License

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