CODEWITHSUNDEEP
pythoncachingmemoization
semekh
semekh

Reputation: 3917

How to cache value of a function in Python?

I have a function, which given an argument calculates a corresponding value and returns it. Returned value of the function, depends only on its parameters, so I'd like to cache (memoize) the value somehow. Furthermore, I also want to be able to invalidate a cached value.

It seems to be a common need, so I am trying to avoid reinventing the wheel.

What I'm looking for is an advanced highly-configurable high-performance library (tool, framework, etc.) and would like the changes to be as lean as possible. Some good points are:

What are some good libraries to use, and how are they compared?

Upvotes: 0

Views: 4899

Answers (2)

Jonathan Vanasco
Jonathan Vanasco

Reputation: 15680

This question doesn't make much sense to me. When you start talking about "high performance" and "concurrent requests" , you're not really talking about using a Python library within an application -- it sounds more like using (or building) some sort of dedicated, external specialized service or daemon.

Personally, I use a mixture of memoization and 'lazy loaded' or 'deferred' properties to define cache gets (and computes). By 'lazy loaded', I mean that instead of always pulling (or computing) cached data, I create a proxy object that has all the information to call the get/create function from the cache on first access. when it comes to database backed material, i've also found it useful to group cache misses and consolidate cache gets - this allows me to load in parallel when possible ( instead of multiple serial requests ).

dogpile.cache takes care of my cache administration (get, set, invalidate), which is configured to store in memcached or dbm ( it allows for several backends ). i use two lightweight objects (12lines?) to handle the deferred gets.

Upvotes: 3

Oleh Prypin
Oleh Prypin

Reputation: 34126

You can use functools.lru_cache, a simple in-memory cache.

An example of cached function:

import functools

@functools.lru_cache()
def f(x, y):
    return x+y

print(f(7, 4))

11

Clear the whole cache:

f.cache_clear()

Clear cache for a particular value (dirty, dirty hack, because there is no direct access to cache dict):

def clear_cache_value(cached_function, *args, **kwargs):
    cache = next(c.cell_contents for c in cached_function.cache_info.__closure__ if isinstance(c.cell_contents, dict))
    del cache[functools._make_key(args, kwargs, False)]

clear_cache_value(f, 7, 4)

Upvotes: 3

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