python don't use cache

Python Doesn't Use Cache

Python is one of the most popular programming languages used by developers around the world. One of the advantages of using python is its flexibility and dynamic nature. However, python does not use cache by default, which can lead to slower execution times.

When writing code in python, it's essential to understand that every time a function is called, it is executed from scratch. This means that the same function called multiple times will run each time with no optimization. This can cause performance issues, especially when dealing with large datasets or complicated algorithms.

Why Doesn't Python Use Cache?

The reason why python does not use cache by default is that it prioritizes flexibility and ease of use over speed. Python is an interpreted language, which means that it executes instructions directly without any precompiled binaries. This allows for a lot of flexibility, but at the cost of speed.

How Can We Improve Performance in Python?

There are several ways to improve performance in python, even without cache. One way is to use optimized libraries and modules for specific tasks. These modules are often written in C or Cython and can provide significant performance improvements over pure python code.

Another way to improve performance is to use caching explicitly. This involves storing the results of expensive computations and reusing them when needed. This can be achieved using built-in tools like functools.lru_cache, which caches the results of a function based on its arguments.


Python does not use cache by default, which can lead to slower execution times. However, there are several ways to improve performance, including using optimized libraries and modules and explicit caching. Understanding these techniques can help developers write more efficient code and improve the performance of their applications.

import functools

def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)