alex gaynor's blago-blog

Your tests are not a benchmark

Posted July 15th, 2013.

I get a lot of feedback about people's experiences with PyPy. And a lot of it is really great stuff for example, "We used to leave the simulation running over night, now we take a coffee break". We also get some less successful feedback, however quite a bit of that goes something like, "I ran our test suite under PyPy, not only was it not faster, it was slower!". Unfortunately, for the time being, this is really expected, we're working on improving it, but for now I'd like to explain why that is.

  • Test runs are short: Your test suite takes a few seconds, or a few minutes to run. Your program might run for hours, days, or even weeks. The JIT works by observing what code is run frequently and optimizing that, this takes a bit of time to get through the "observer phase", and during observation PyPy is really slow, once observation is done PyPy gets very very fast, but if your program exits too quickly, it'll never get there.
  • Test code isn't like real code: Your test suite is designed to try to execute each pieces of code in your application exactly once. Your real application repeats the same task over and over and over again. The JIT doesn't kick in until a piece of code has been run over 1000 times, so if you run it just a small handful of times, it won't be fast.
  • Test code really isn't like real code: Your test code probably does things like monkeypatch modules to mock things out. Monkeypatching a module will trigger a bit of deoptimization in PyPy. Your real code won't do this and so it will be fully optimized, but your test suite does hit the deoptimization and so it's slow.
  • Test code spends time where app code doesn't: Things like the setup/teardown functions for your tests tend to be things that are never run in your production app, but sometimes they're huge bottlenecks for your tests.
  • Test suites often have high variability in their runtimes: I don't have an explanation for why this is, but it's something observed over a large number of test suites. Bad statistics make for really bad benchmarks, which makes for bad decision making.

If you want to find out how fast PyPy (or any technology) is, sit down and write some benchmarks, I've got some advice on how to do that.

This entry was tagged with python.
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