[bite_victim]

I am a bit reluctant about asking this but where do we stand in terms of real world performance? This is such a broad topic, I know, but surely there has to be some in-house metric system about this (perhaps popular web and scientific frameworks, game dev frameworks etc).

[dalke]

The answer is "all over the place". It really does depend on your benchmarks and use case. There is no aggregate real-world benchmark. Such a thing is very hard to do, and not all that portable.

For example, in one performance intensive code that I use, while mostly written in Python, has a C extension, which itself uses inline assembly for the core routine. Similarly, most numeric calculations that use NumPy will end up doing BLAS / LAPACK / ATLAS calls, so the performance limitations aren't due to C. The switch from 2 to 3 isn't really going to affect these.

If it was seriously and consistently worse, eg, for Django, then it's likely we would heard about it by now.

Instead, there are reports like https://www.youtube.com/watch?v=f_6vDi7ywuA (slides at https://speakerdeck.com/pyconslides/python-3-dot-3-trust-me-... ) from PyCon 2013. At minute 30:54 / slide 16 the speaker evaluates the two and finds that the median performance was effectively identical for microbenchmarks, and ~5% slower on macrobenchmarks, though even then it varies from 27% slower to 13% faster depending on the specifics, and as the speaker points out, the tested packages aren't doing exactly the same things on both versions.

In any case, that was 2 years ago.

[88e282102ae2e5b]

An image-reading tool I maintain works 14 times faster in Python 3.4 compared to Python 2.7. I have no way of accounting for the difference though.

[dr_zoidberg]

This is probably the first time I heard of such a speedup when going from 2.7 to 3.x, and the fact that you can't account for it makes it worse because we can't evaluate what's happening and if such speedup could be achievable on other projects :(