Is Python ready for the Enterprise?
summary
Claims that Python meets enterprise needs by addressing typing GIL and speed with concrete tools and measurements: optional static typing via PEP 484 plus mypy for checks, GIL avoided by using multiprocessing or C extensions, and performance improved with Cython 72x examples and JITs like Numba. A working case study is Caterpillar search engine using NumPy and 100% tests indexing English Wikipedia at about 1/6 Lucene speed without optimization, showing workable tradeoffs between developer velocity and runtime speed.
AI-written summary · about
chapters (30)
- 0:00Introduction
- 0:29Ryans background
- 0:58Outline
- 1:27Enterprise requirements
- 2:22Enterprise grade
- 3:06Python today
- 4:07Dynamic vs static typing
- 6:03Tiepins
- 7:06Arguments
- 7:42Speed
- 8:40Cost
- 9:22C
- 11:08siphon
- 12:07ipython notebooks
- 13:00Other Python tools
- 13:54Speed problem
- 14:32Scaling Python
- 15:58Gill only applies
- 16:56No Gil
- 17:34Concurrency is easy
- 18:42Summary
- 19:37Capisce
- 20:53Why did you choose Python
- 22:46Visualization
- 23:36API
- 23:46Adding a document
- 24:20Test Coverage
- 24:53Public Service Announcements
- 25:46Advice Forum
- 26:52Other Questions
related talks
49mPython Enterprise: a Wind of Freedom
45mPython for Speed, Scale, and Science
01h 00mKeynote: Python now and in the future
22mPyPy and the future of the Python ecosystem
48mPython Performance: Past, Present and Future
26mHow to Succeed with Python Across the Enterprise
58mPython performance Past, Present, Future
44mPython's Bright Future in Science