The Python interpreter plays a critical role in controlling the performance of your code, using a vast variety of optimizations & fast paths for common code patterns and idioms. This talk will be a fun interactive session presented through code examples that display an assortment of those optimizations and in which (unexpected) ways they can break, worsening the performance of your Python code; we'll follow by inspecting CPython interpreter's inherent behavior to understand the reason for the breakage. The output and results just might surprise even the most advanced Python coders.
This talk will (partly) be based on some code samples I have contributed to the excellent wtfpython (https://github.com/satwikkansal/wtfpython) focused on Python quirks in general, with a touch of performance. Through these snippets we will learn some performance dos and don'ts, by understanding CPython internals and features under the hood.
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Yonatan Goldschmidt is a Team Lead at Granulate, overseeing the development and deployment of their real-time continuous optimization solution as an expert in low-level programming. Before joining Granulate, Yonatan served for nearly six years in the Israel Defense Forces as a Team Lead and R&D Specialist.