What are the performance impacts of caching in Python?

What are the performance impacts of caching in Python?

Caching in Python can significantly improve performance by storing results of expensive function calls and reusing them.

Can you give me an example of how caching works in Python?

Sure! You can use the functools.lru_cache decorator to cache results of a function. For example:

from functools import lru_cache

@lru_cache(maxsize=None)
def expensive_function(x):
    # Simulate a costly computation
    return x * x

What are the trade-offs of using caching?

The trade-offs include increased memory usage and potential stale data if the cached results are not invalidated properly.

When should I avoid using caching?

Avoid caching when the data changes frequently, as it can lead to outdated information being served.