What I mostly care about for speeding up our Python programs

April 17, 2017

There are any number of efforts and technologies around these days that try to speed up Python, starting with the obvious PyPy and going on to things like Cython and grumpy. Every so often I think about trying to apply one of them to the Python code I deal with, and after doing this a few times (and even making some old experiments with PyPy) I've come to a view of what's important to me in this area.

(This has come to be more and more on my thoughts because these days we run at least one Python program for every incoming email from the outside world. Sometimes we run more than that.)

What I've come around to caring about most is reducing the overall resource usage of short running programs that mostly use the Python standard library and additional pure-Python modules. By 'resource usage' I mean a combination of both CPU usage and memory usage; in our situation it's not exactly great if I make a program run twice as fast but use four times as much memory. In fact for some programs I probably care more about memory usage than CPU, because in practice our Python-based milter system probably spends most of its time waiting for our commercial anti-spam system to digest the email message and give it a verdict.

(Meanwhile, our attachment logger program is probably very close to being CPU bound. Yes, it has to read things off disk, but in most cases those files have just been written to disk so they're going to be in the OS's disk cache.)

I'm also interested in making DWiki (the code behind Wandering Thoughts) faster, but again I actually want it to be less resource-intensive on the systems it runs on, which includes its memory usage too. And while DWiki can run in a somewhat long-running mode, most of the time it runs as a short-lived CGI that just serves a single request. DWiki's long-running daemon mode also has some features that might make it play badly with PyPy, for example that it's a preforking network server and thus that PyPy is probably going to wind up doing a lot of duplicate JIT translation.

I think that all of this biases me towards up-front approaches like Cython and grumpy over on the fly ones such as PyPy. Up-front translation is probably going to work better for short running programs (partly because I pay the translation overhead only once, and in advance), and the results are at least reasonably testable; I can build a translated version and see in advance whether the result is probably worth it. I think this is a pity because PyPy is likely to be both the easiest to use and the most powerful accelerator, but it's not really aimed at my usage case.

(PyPy's choice here is perfectly sensible; bigger, long-running programs that are actively CPU intensive for significant periods of time are where there's the most payoff for speeding things up.)

PS: With all of this said, if I was serious here I would build the latest version of PyPy by hand and actually test it. My last look and the views I formed back then were enough years ago that I'm sure PyPy has changed significantly since then.

Written on 17 April 2017.
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Last modified: Mon Apr 17 02:05:16 2017
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