Code Smell 250 — Premature Memoization
Memoization is awesome. Let’s abuse it
TL;DR: Don’t apply premature optimization too early
Problems
- Readability
- Code Complexity
- Premature Optimization
- Obscured Logic
Solutions
- Apply memoization in actual real business situations and measure its impact through empirical benchmarks.
Context
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
Wrong
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.Right
def factorial(n):
if n == 0:
return 1
return n * factorial(n-1)Detection
[X] Semi-Automatic
You can search for all places where you are using this technique and validate if they are worth it.
Exceptions
- Real performance problems with strong factual evidence
Tags
- Premature Optimization
Level
[X] Intermediate
AI Generation
Unless you explicitly ask the IAs to use this technique, they will suggest cleaner solutions.
AI Detection
ChatGPT, Gemini, and Claude.ai detect some problems with this technique but do not mention readability as a concern.
Conclusion
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.
Relations
More Information
Disclaimer
Code Smells are my opinion.
Credits
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.
