Caching and Memoization

Caching and memoization are powerful techniques that can significantly improve the performance of our applications. By storing the results of expensive function calls and reusing them when needed, we can avoid redundant computations and speed up our code.

What is Caching?

Caching involves storing the results of expensive function calls in a cache, which is a temporary storage space. When a function is called with the same set of input parameters, the cached result is returned instead of recomputing the result.

How does caching work?

In Python, we can use the functools.cache decorator to implement function caching. This decorator automatically caches the result of a function and returns the cached result when the same arguments are passed to the function.

Here's an example of using caching with the Fibonacci sequence:

1import functools
2
3@functools.cache
4
5def fibonacci(n):
6    if n < 2:
7        return n
8    return fibonacci(n - 1) + fibonacci(n - 2)
9
10print(fibonacci(10))  # The result is cached
11print(fibonacci(10))  # The cached result is returned

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import functools

​

@functools.cache

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# Python logic here