Understanding how your Python application is using memory can be tough. First, Python has it's own layer of reused memory (arenas, pools, and blocks) to help it be more efficient. And many important Python packages are built in natively compiled languages like C and Rust often times making that section of your memory opaque. But with Memray, you can way deeper insight into your memory usage. We have Pablo Galindo Salgado and Matt Wozniski back on the show to dive into Memray, the sister project to their pystack one we recently covered.

Links from the show

Pablo Galindo Salgado: @pyblogsal

Matt Wozniski: github.com

pytest-memray: github.com

PEP 669 – Low Impact Monitoring for CPython: peps.python.org

Memray discussions: github.com

Mandlebrot Flamegraph example: bloomberg.github.io

Python allocators: bloomberg.github.io

Profiling in Python: docs.python.org

PEP 693 – Python 3.12 Release Schedule: peps.python.org

Watch this episode on YouTube: youtube.com

Episode transcripts: talkpython.fm

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