On Fri, 25 Nov 2011 20:34:21 +1100 Matt Joiner <anacro...@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@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. _______________________________________________ Python-Dev mailing list Python-Dev@python.org http://mail.python.org/mailman/listinfo/python-dev Unsubscribe: http://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
