Wednesday, June 8, 2011

Report back from our survey

Hi all,

I'm here to report back the results of our survey. First, we're very pleased to report that a number of you guys are happilly running PyPy in production! Most (97%) of the respondants using PyPy are using it because it's faster, but a further 26% (respondants could choose multiple answers) are using it because of lower memory usage. Of users who aren't using PyPy, the most common reason was C extensions, followed by "Other".

From reading the extra comments section there are a few things we've learned:

  1. Google docs needs a better UI for this stuff
  2. A huge number of people want NumPy and SciPy, it was easily the most requested C extension (25% of respondants said somthing about NumPy). We've already blogged on the topic of our plans for NumPy.
  3. Having packages in the various OS's repositories would be a big help in getting users up and running.

A huge thanks to everyone who responded! Finally, if you're using PyPy in production we'd love to get a testimonial from you, if you're willing to spare a few minutes to give us a quote or two please get in contact with us via our mailing list.

Thanks, Alex

12 comments:

Paul said...

I'm surprised more people didn't mention Python 3 support as a big breaker. I certainly did.

said...

"... we're very pleased to report that a number of you guys are happilly running PyPy in production"

You decided to keep the actual number of users a secret? Why?

Maciej Fijalkowski said...

@⚛ I think Alex was simply too lazy to count :-) At some point there were 600 respondents and roughly 10% of them used pypy in production, which is pretty good IMO.

said...

@Maciej Fijalkowski: Ok, thanks for the clarification.

Marko Tasic said...
This comment has been removed by the author.
Marko Tasic said...

I'm using pypy 1.5 with jit in production for highly reliable and responsive distributed and decentralized systems, and I'm happy with it.

said...

@Marko Tasic: If I may ask a question. You wrote that you are using PyPy for highly reliable systems. I know what you mean, but it seems to me that certain features of Python are in contradiction with high reliability. For example, it is in practice impossible to know at compile-time whether you misspelled a variable or parameter in Python source code. My question would be: why are you using a language which has only rudimentary compile-time error detection to implement a high reliability system?

Maciej Fijalkowski said...

@⚛ Not even trying to argue with you, comments on this blog is not a proper place to discuss whether Python is good for high-reliability systems. Please take the discussion somewhere else

Thanks,
fijal

said...

@Maciej Fijalkowski: I will of course do what you ask, but I would like you to point me to at least one blog comment that: (1) Is initially saying that Python/PyPy is *good* for task X, and (2) You or somebody else from the PyPy team wrote "Please take the discussion about X somewhere else".

Thanks

Maciej Fijalkowski said...

@⚛ The line might be blurry, but "I'm using PyPy for X" or "I'm not using PyPy for X, because ..." is on topic. While "Python can be used for X" or "Python can't be used for X, because ..." is not on topic. This is a fine line between language implementation (which is PyPy about) and language design (which PyPy is not about, python-dev/python-list/python-ideas mailing lists are about that).

Cheers,
fijal

Anonymous said...

What about a FFI to C or C++? Something like LuaJit's FFI, which is really good.

Anonymous said...

Lack of support for numpy and scipy are what keep me from using pypy. Am using python for analysis of ultra high throughput DNA sequencing data.

Would be very curious to see how much performance I could gain by using pypy.