How python uses the decorator to cache function calls

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Use the decorator to cache function calls

Have you ever written a function that performs expensive I _ swap O operations or some fairly slow recursion that might benefit from caching (storing) its results? If you do, then there is a simple solution, that is, to use functools's lru_cache:

From functools import lru_cache import requests @ lru_cache (maxsize=32) def get_with_cache (url): try: r = requests.get (url) return r.text except: return "Not Found" for url in ["https://google.com/"," https://martinheinz.dev/", "https://reddit.com/", "https://google.com/"," https://dev.to/martinheinz", "https://google.com/"]: get_with_cache (url) print (get_with_cache.cache_info ()) # CacheInfo (hits=2, misses=4, maxsize=32, currsize=4)

In this example, we use cacheable GET requests (up to 32 cached results). You can also see that we can use the cache_info method to check the cache information of the function. The decorator also provides a clear_cache method to invalidate the cached results.

I would also like to point out that this function should not be used with functions that have side effects or with functions that create mutable objects with each call.

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