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[Python-Dev] file.readinto performance regression in Python 3.2 vs. 2.7?

Antoine Pitrou solipsis at pitrou.net
Fri Nov 25 12:04:00 CET 2011 
On Fri, 25 Nov 2011 20:34:21 +1100
Matt Joiner <anacrolix at gmail.com> wrote:
> 
> It's Python 3.2. I tried it for larger files and got some interesting results.
> 
> readinto() for 10MB files, reading 10MB all at once:
> 
> readinto/2.7 100 loops, best of 3: 8.6 msec per loop
> readinto/3.2 10 loops, best of 3: 29.6 msec per loop
> readinto/3.3 100 loops, best of 3: 19.5 msec per loop
> 
> With 100KB chunks for the 10MB file (annotated with #):
> 
> matt at stanley:~/Desktop$ for f in read bytearray_read readinto; do for
> v in 2.7 3.2 3.3; do echo -n "$f/$v "; "python$v" -m timeit -s 'import
> readinto' "readinto.$f()"; done; done
> read/2.7 100 loops, best of 3: 7.86 msec per loop # this is actually
> faster than the 10MB read
> read/3.2 10 loops, best of 3: 253 msec per loop # wtf?
> read/3.3 10 loops, best of 3: 747 msec per loop # wtf??

No "wtf" here, the read() loop is quadratic since you're building a
new, larger, bytes object every iteration.  Python 2 has a fragile
optimization for concatenation of strings, which can avoid the
quadratic behaviour on some systems (depends on realloc() being fast).

> readinto/2.7 100 loops, best of 3: 8.93 msec per loop
> readinto/3.2 100 loops, best of 3: 10.3 msec per loop # suddenly 3.2
> is performing well?
> readinto/3.3 10 loops, best of 3: 20.4 msec per loop

What if you allocate the bytearray outside of the timed function?

Regards

Antoine.
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