Boost Python Code Performance with Caching: Exploring Techniques and Tools

In this video we delve into caching in Python, exploring how it works and its potential to improve performance.

By implementing caching, we store the results in a cache dictionary, associating them with the corresponding arguments. Subsequent function calls with the same arguments retrieve the results from the cache, eliminating the need for computing the values again which significantly boosts performance.

Additionally, external packages like request_cache provide tools to cache network requests using a local (SQLite) database, enabling faster subsequent retrieval of data. We show how we have implemented this in our pybites-search tool and how it speeds up searching Pybites content 😍

Using caching we can enhance performance and create more efficient Python programs. We hope you'll embrace it and please comment below when you do ... 💪🔥

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And last but not least, we appreciate any feedback to make our YouTube content better 💡

[CJ-jsvx]

can this cache lead to wrong results unintentionally as it skips actual computation?