Why are Python and Ruby slow?
summary
Explains why JIT and VM architecture matter with concrete numbers: the nbody benchmark shows C++ ~21 ms versus Python/Ruby >3 s (~100×), PyPy gives ~10× speedup, and a Rails monolith gains at most ~3×; demonstrates use of PyPy, Numba, CPython, V8 and native C++ extensions and why those keep hot paths fast; diagnoses the bottleneck as memory/cache behavior and the Global interpreter lock and outlines ongoing fixes like subinterpreters and GIL removal efforts plus practical tradeoffs for real systems.
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chapters (12)
- 0:00Slow language myths
- 1:45CPU execution and caching
- 5:00VMs and language semantics
- 7:50JavaScript vs VM speed
- 9:40Nbody benchmark comparison
- 11:53Rich semantics slow projects
- 13:22Python/Ruby as glue code
- 16:17Industry targets for Python/Ruby
- 18:36Future performance improvements
- 20:02Python GIL and sub-interpreters
- 22:17Developer time vs speed
- 23:43Learning curve and maintainability of rich languages
