Great news, Martin! On Tue, Sep 27, 2011 at 3:56 PM, "Martin v. Löwis" <mar...@v.loewis.de> wrote: > I have redone my memory benchmark, and added a few new > counters. > > The application is a very small Django application. The same > source code of the app and Django itself is used on all Python > versions. The full list of results is at > > http://www.dcl.hpi.uni-potsdam.de/home/loewis/djmemprof/ > > Here are some excerpts: > > A. 32-bit builds, storage for Unicode objects > 3.x, 32-bit wchar_t: 6378540 > 3.x, 16-bit wchar_t: 3694694 > PEP 393: 2216807 > > Compared to the previous results, there are now some > significant savings even compared to a narrow unicode build. > > B. 3.x, number of strings by maxchar: > ASCII: 35713 (1,300,000 chars) > Latin-1: 235 (11,000 chars) > BMP: 260 (700 chars) > other: 0 > total: 36,000 (1,310,000 chars) > > This explains why the savings for shortening ASCII objects > are significant in this application. I have no good intuition > how this effect would show for "real" applications. It may be > that the percentage of ASCII strings (in number and chars) grows > proportionally with the total number of strings; it may also > be that the majority of these strings is a certain fixed overhead > (resulting from Python identifiers and other interned strings). > > C. String-ish objects in 2.7 and 3.3-trunk: > 2.x 3.x > #unicode 370 36,000 > #bytes 43,000 14,000 > #total 43,400 50,000 > > len(unicode) 5,300 1,306,000 > len(bytes) 2,040,000 860,000 > len(total) 2,046,000 2,200,000 > > (Note: the computations in the results are slightly messed up: > the number of bytes for bytes objectts is actually the sum > of the lengths, not the sum of the sizeofs; this gets added > in the "total" lines to the sum of sizeofs of unicode strings, > which is non-sensical. The table above corrects this) > > As you can see, Python 3 creates more string objects in total. > > D. Memory consumption for 2.x, 3.x, PEP 393, accounting both > unicode and bytes objects, using 32-bit builds and 32-bit > wchar_t: > 2.x: 3,620,000 bytes > 3.x: 7,750,000 bytes > PEP 393: 3,340,000 bytes > > This suggests that PEP 393 actually reduces memory consumption > below what 2.7 uses. This is offset though by "other" (non-string) > objects, which take 300KB more in 3.x. > > Regards, > Martin > _______________________________________________ > 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/guido%40python.org >
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