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extradoc / blog / draft / so_you_want.rst

So you want to try PyPy?


During the PyCon trip multiple people asked me how exactly they could run their stuff on PyPy to get the speedups. Now, in an ideal world, you would just swap CPython with PyPy, everything would run tons of times faster and everyone would live happily ever after. However, we don't live in an ideal world and PyPy does not speed up everything you could potentially run. Chances are that you can run your stuff quite a bit faster, but it requires quite a bit more R&D than just that. This blog post is an attempt to explain certain steps that might help. So here we go:

  • Download and install PyPy. 2.0 beta 1 or upcoming 2.0 beta 2 would be a good candidate; it's not called a beta for stability reasons.
  • Run your tests on PyPy. There is absolutely no need for fast software that does not work. There might be some failures. Usually they're harmless (e.g. you forgot to close the file); either fix them or at least inspect them. In short, make sure stuff works.
  • Inspect your stack. In particular, C extensions, while sometimes working, are a potential source of instability and slowness. Fortunately, since the introduction of `cffi`_, the ecosystem of PyPy-compatible software has been growing. Things I know are written with PyPy in mind:
  • Have benchmarks. If you don't have benchmarks, then performance does not matter for you. Since PyPy's warm-up time is bad (and yes, we know, we're working on it), you should leave ample time for warm-ups. Five to ten seconds of continuous computation should be enough.
  • Try them. If you get lucky, the next step might be to deploy and be happy. If you're unlucky, profile and try to isolate bottlenecks. They might be in a specific library or they might be in your code. The better you can isolate them, the higher your chances of understanding what's going on.
  • Don't take it for granted. PyPy's JIT is very good, but there is a variety of reasons that it might not work how you expect it to. A lot of times it starts off slow, but a little optimization can improve the speed as much as 10x. Since PyPy's runtime is less mature than CPython, there are higher chances of finding an obscure corner of the standard library that might be atrociously slow.
  • Most importantly, if you run out of options and you have a reproducible example, please report it. A `pypy-dev`_ email, popping into #pypy on irc.freenode.net, or getting hold of me on twitter are good ways. You can also contact me directly at fijall at gmail.com as well. While it's cool if the example is slow, a lot of problems only show up on large and convoluted examples. As long as I can reproduce it on my machine or I can log in somewhere, I am usually happy to help.
  • I typically use a combination of `jitviewer`_, `valgrind`_ and `lsprofcalltree`_ to try to guess what's going on. These tools are all useful, but use them with care. They usually require quite a bit of understanding before being useful. Also sometimes they're just plain useless and you need to write your own analysis.

I hope this summary of steps to take is useful. We hear a lot of stories of people trying PyPy, most of them positive, but some of them negative. If you just post "PyPy didn't work for me" on your blog, that's cool too, but you're missing an opportunity. The reasons may vary from something serious like "this is a bad pattern for PyPy GC" to something completely hilarious like "oh, I left this sys._getframe() somewhere in my hot loops for debugging" or "I used the logging module which uses sys._getframe() all over the place".

Cheers, fijal