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Tangential, but I practically owe my life to this guy. He wrote the flask mega tutorial in what I followed religiously to launch my first website. Then right before launch, in the most critical part of my entire application; piping a fragged file in flask. He answered my stackoverflow question, I put his fix live, and the site went viral. Here's the link for posterity's sake https://stackoverflow.com/a/34391304/41802…
You have made my day, sir. :)
What are the reasons why nobody uses pypy?
We look periodically and pypy is usually unusable for us due to third-party library support. E.g. psycopg2, at least as of a couple years ago. Have not checked in a while.
Tangential, but I practically owe my life to this guy. He wrote the flask mega tutorial in what I followed religiously to launch my first website. Then right before launch, in the most critical part of my entire application; piping a fragged file in flask. He answered my stackoverflow question, I put his fix live, and the site went viral. Here's the link for posterity's sake https://stackoverflow.com/a/34391304/41802…
You have made my day, sir. :)
Thank you for the work you put in.
Earlier quoted context omitted.
Yes, I'm consistent in that. What I don't get is if that's the case, why is there such a focus on improving Python perf? At best they're getting marginal improvements on something that most Python devs claim they don't care about, and which they say is not important for Python as a language due to JIT, C interop, and so on.
I think perhaps their hope is that eventually Python can get to Go-level if not Rust-level performance if they keep up the optimizations. I do personally believe this to be possible. The motivating example is Julia, which is a high level language with low-level language's performance. After arriving there, developers will care.
https://news.ycombinator.com/item?id=45524485
Particularly this part is relevent to the Python discussion:
What is Julia's central conceit? It aims to solve "the two language" problem, i.e. the problem where prototyping or rapid development is done in a dynamic and interactive language like Python or MATLAB, and then moved for production to a faster and less flexible language like Rust or C++.
This is exactly what the speaker in the talk addresses. They are still using Julia for prototyping, but their production use of Julia was replaced with Rust. I've heard several more anecdotal stories of the exact same thing occurring. Here's another high profile instance of Julia not making it to production:
https://discourse.julialang.org/t/julia-used-to-prototype-wh...
Julia is failing at its core conceit.
The reason I bring this up is because there's a lot of "cake having/eating" floating around these types of conversations -- that's it's possible to be all the things, without a healthy discussion of what the tradeoffs are in going that direction, and what that would me mean for the people who are happy with the way things are. These little % gains are all Python is going to achieve without actually asking the developer to sacrifice their development process in some way.
Tangential, but I practically owe my life to this guy. He wrote the flask mega tutorial in what I followed religiously to launch my first website. Then right before launch, in the most critical part of my entire application; piping a fragged file in flask. He answered my stackoverflow question, I put his fix live, and the site went viral. Here's the link for posterity's sake https://stackoverflow.com/a/34391304/41802…
You have made my day, sir. :)
> And this is a bit disappointing. At least for this test, the JIT interpreter did not produce any significant performance gains, so much that I had to double and triple check that I used a correctly built interpreter with this feature enabled. I do not know much about the internals of the new JIT compiler, but I'm wondering if it cannot deal with this heavily recursive function. FWIW one thing that is worth calling…
I mean, Guido had a 2021 Faster CPython presentation where they claimed "5x in 4 years (1.5x per year)"[0]. Developers have significantly walked back those expectations since then. [0] Github slide deck https://github.com/faster-cpython/ideas/blob/main/FasterCPyt...
Didn't help Microsoft axed several folks on that team too...
Earlier quoted context omitted.
I've been writing Python professionally for a couple of decades, and there've only been 2-3 times where its performance actually mattered. When writing a Flask API, the timing usually looks like: process the request for .1ms, make a DB call for 300ms, generate a response for .1ms. Or writing some data science stuff, it might be like: load data from disk or network for 6 seconds, run Numpy on it for 3 hours, write it…
That's because you're doing web stuff. (I/O limited). So much of our computing experience has been degraded due to this mindset applied more broadly. Despite a steady improvement in hardware, my computing experiences have been stagnating and degraded in terms of latency, responsiveness etc. I'm not going to even go into the comp chem simulations I've been running, or that about 1/3 the stuff I do is embedded. I do st…
What are the reasons why nobody uses pypy?
It doesn't play nice with a lot of popular Python libraries. In particular, many popular Python libraries (NumPy, Pandas, TensorFlow, etc.) rely on CPython’s C API which can cause issues.
That said, of all the reasons stated here, it's why I don't primarily use PyPy (lots of libraries still missing)