Memoization in Python

Fri, 17 Apr 2020 09:33:01 +0000

Memoization is a optimization technique frequently used in functional programming. It means storing the result of a computationally intensive function call and returing the cached result when the function is called with the same arguments.

Python 3.2 introduced the functools.lru_cache decorator which enables memoization of function results in Python.

Using lru_cache is simple as we can see from a example taken from the Python documentation

@lru_cache
def count_vowels(sentence):
    sentence = sentence.casefold()
    return sum(sentence.count(vowel) for vowel in 'aeiou')

When calling for ex. count_vowels('Count vowels in this sentence') the result is cached and when you call the function with the same argument the cached result is returned instead of counting the vowels again. This can be a huge performance boost when dealing with big datasets with potentially similar data.

Optimization on dev notes

Memoization in Python