# Code Smell 250 - Premature Memoization

> TL;DR: Don't apply [premature optimization](https://maximilianocontieri.com/code-smell-20-premature-optimization) too early

# Problems

- Readability

- Code Complexity

- Premature Optimization

- Obscured Logic

# Solutions

1. Apply [memoization](https://en.wikipedia.org/wiki/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

[Gist Url]: # (https://gist.github.com/mcsee/7cdd5907fa5404a79581a3fac7a2af65)

```python
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

[Gist Url]: # (https://gist.github.com/mcsee/3aa806cc9c3d9eb8493706ec031f7343)

```python
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

%[https://maximilianocontieri.com/code-smell-06-too-clever-programmer]

%[https://maximilianocontieri.com/code-smell-20-premature-optimization]

# More Info

%[https://en.wikipedia.org/wiki/Memoization]

# Disclaimer

Code Smells are my [opinion](https://maximilianocontieri.com/i-wrote-more-than-90-articles-on-2021-here-is-what-i-learned).

# Credits

Photo by [Steffen Lemmerzahl](https://unsplash.com/@steffen_l) on [Unsplash](https://unsplash.com/photos/snow-covered-field-during-daytime-kp1n4gWKTOg)  
  
* * *

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

_Rico Mariani_ 
 
%[https://maximilianocontieri.com/software-engineering-great-quotes]

* * *

This article is part of the CodeSmell Series.

%[https://maximilianocontieri.com/how-to-find-the-stinky-parts-of-your-code]