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Comment by wyldfire | Hacker News Reader

I never understood why it didn't completely take over. It worked flawlessly for every use case I tried.

It always seemed to me like it a "free" way to make it faster. Why would it matter whether it was endorsed by the BDFL or PSF?

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It doesn’t work with psycopg or many other libraries that use c extensions. That’s the main reason.

Only because they were compiled against cpython and not pypy right? If pypy had taken off/over, wouldn't it have been the other way around, where you need to do a special compile any time you want to use a C project with cpython rather than using the prebuilt pypy-compatible version?

Edit: things seem to have changed since I last looked into compiling a dependency for pypy. https://doc.pypy.org/en/latest/faq.html#do-c-extension-modul... now says they support c-extension modules without modifications

The package you mentioned, some postgres thing apparently, mentions they support pypy 3.9 and 3.10: https://www.psycopg.org/psycopg3/docs/basic/install.html

"Only": https://github.com/psycopg/psycopg/issues/154

By “some postgres thing”, you mean…pretty much the only way that one would connect to Postgres in Python!

Right, I just wasn't familiar with it (never heard this name before) and the only association I could think of for this abbreviated name was psyops and cops. Turns out it's actually for postgres which, of course, is a name I very well know. Didn't realize it was a well-known library!

There's also https://github.com/MagicStack/asyncpg

Oh, I'd thought some significant effort had gone into emulating the CPython C extension interface.

But maybe even with that, it's unable to support some extensions?

     If you want your code to run faster, you should probably just use PyPy.

Guido Van Rossum (once CPython's BDFL) said that.

At PyCon 2015:

* https://www.youtube.com/watch?v=2wDvzy6Hgxg&t=16m52s

If someone suggested switching to a new C++ compiler with 99% of the features of the C++ standard with the selling point that it ran template metaprograms 100x faster, could you convince your company to switch? In the setting where python shines and performance matters, python is serving as the metalanguage executed at compile time for a strictly typed compiled language such as SQL, torch graph traces, jax, or z3

that only wins when it will lower costs significantly (percentage-wise) at my company :) . We don't care how much more green it would be, how fast it is, our main thing is always "how much does it cost" with everything converted to $$ (dev time, server cost, etc) and it can't change uptime, we would lose customers quickly without uptime, we have a fickle bunch.

pypy has never kept up with cPython features and releases. it was always well behind the curve and did not provide essential behaviors in a timely fashion. plus it can't run any C extensions or cython code (or has to run them with emulations that erase the speed benefits), which takes a big chunk out of the available ecosystem. Example: the situation with numpy: https://doc.pypy.org/en/latest/faq.html#what-about-numpy-num...

It keeps up pretty well, I think. But given what's in a version of Python is "what's implemented in CPython at that version", I don't see how it's possible to keep up very easily at all.

this is nonsense, cPython's development is very public and works on a multi-year schedule. It's not like Python 3.5.0 is released and that's when everyone knows what features it will have, each major version goes through at least a year of alphas and betas. Pypy spends a few years behind that and on top of it sometimes introduces new bugs that have to be reported and fixed. this is not anyone's fault, it's just for what pypy is doing to be successful, they would need a much larger maintenance ecosystem which they just dont have. The contributor ecosystem for cpython is literally ten times larger than that of pypy.

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