Comment by vapemaster | Hacker News Reader

Comment by vapemaster | Hacker News Reader

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what is the split that has to do with 2vs3? Really it just seems like some devs are still griping about fairly minor backwards compatibility issues. the whole print vs print() is obviously a straw man. but the major one, strings, unicode, and bytes, changes for the good in py3. the fact that people have to go back and "un-hack" their code is time, effort, and more commits but it's for the best.

<rant> i think the real split is between CPython + C-Extensions and the JIT (PyPy,Numba) gang for heavy lifting in python. I for one wish Guido would embrace the effort of integrating the GILectomy into CPython and make python thread parrallism real for CPU bound work as opposed to the high overhead multiprocessing approach.

Also, including mypy in the stdlib to make genuine optional static typing would make python a defacto standard for much of what it does now without the "we love python but...." production code pain points. </rant>

I don't think the whole print vs print() problem is a straw man (or similarly the division operator switch) apart from being relatively easy to fix or avoid by importing from __future__, but crucial in understanding the attitude the language owners have for its users. They could have kept print and introduced printf(), but they didn't. They didn't even get rid of the statement vs. expression distinction, which would have been a compelling reason to break print (and would give us real lambdas).

The split you mention is real, but it's existed even longer than the 2vs3 split. All the various alternate Python implementations that have come and gone or are still around with the exception of Numba were started pre-3k. I wouldn't even argue that upstream was necessarily wrong not to prioritize performance and more specific use cases (like certain production uses, or scientific uses (a lot of begging to finally get the @ operator)), it just didn't matter as much for a long time. But things are different now. There are many other very expressive and much faster (either dynamically typed or static) languages in competition that lessen Python's effectiveness at reducing dev time in exchange for more hardware. Without a more serious focus on performance, Python will be driven only by momentum. That can last a long time, and is essentially the end point anyway when performance is "good enough" compared to the close alternatives, but it's not a great look when there's still much that could be done.

So, you see, I know a bit about writing efficient code. I have held a firm belief, and still do, that if you're doing heavy lifting in CPython, you're doing it wrong, and better parallelism will not significantly help you.

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