Code Smell 250 - Premature Memoization

Memoization is awesome. Let's abuse it

TL;DR: Don't apply premature optimization too early


  • Readability

  • Code Complexity

  • Premature Optimization

  • Obscured Logic


  1. Apply memoization in actual real business situations and measure its impact through empirical benchmarks.


Memoization can help you improve the performance of recursive functions involving redundant computations but compromise code readability and maintainability

It would help if you only used it with strong factual evidence on real business case scenarios.

Sample Code


memo = {}
def factorial_with_memo(n):
    if n in memo:
        return memo[n]
    if n == 0:
        return 1
    result = n * factorial_with_memo(n-1)
    memo[n] = result
    return result

  # This function optimizes the computation of factorials
  # by storing previously computed values,
  # Reducing redundant calculations 
  # and improving performance for large inputs.


def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n-1)


[X] Semi-Automatic

You can search for all places where you are using this technique and validate if they are worth it.


  • Real performance problems with strong factual evidence


  • Premature Optimization


[X] Intermediate

AI Generation

Unless you explicitly ask the IAs to use this technique, they will suggest cleaner solutions.

AI Detection

ChatGPT, Gemini, and detect some problems with this technique but do not mention readability as a concern.


It would be best if you kept a balance between performance optimization and code clarity.

You can consider alternatives such as iterative approaches or algorithmic optimizations since memoization significantly compromises code readability.


More Info


Code Smells are my opinion.


Photo by Steffen Lemmerzahl on Unsplash

A cache with a bad policy is another name for a memory leak.

Rico Mariani

This article is part of the CodeSmell Series.