Hot paths: using Rust libraries in Python
summary
Presentation examines using Rust libraries to speed up Python backend services by identifying performance-critical “hot paths” and rewriting only those functions in a compiled language. It compares this targeted approach against alternatives (C, C++, Cython, Numba) and focuses specifically on integrating Rust with Python to improve per-core RPS without rewriting entire microservices.
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chapters (9)
- 0:00Introduction
- 1:29Python performance issues
- 10:48Python delays causes
- 19:27Rust CLI tools
- 22:59Ruff tool for Python
- 26:31Narwhal library for Python
- 33:53Other Rust libraries for Python
- 35:19Summary
- 36:03Q&A
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