Antoine Pitrou added the comment: Note the problem is easily reproduce. For example we can take numpy where some ufuncs (i.e. function-like objects implemented in C) don't have a __module__.
$ PYTHONHASHSEED=0 python3.4 -m timeit -s "import pickle, numpy; d=numpy.add" "pickle.dumps(d)" 1000 loops, best of 3: 280 usec per loop $ PYTHONHASHSEED=0 python3.4 -m timeit -s "import pickle, numpy; d=numpy.diff" "pickle.dumps(d)" 100000 loops, best of 3: 2.74 usec per loop We see that pickling numpy.add (which doesn't have a __module__) is 100x slower than numpy.diff (which has a __module__)! Note I'm forcing PYTHONHASHSEED for consistent results, since whichmodule() uses dict iteration. For comparison, 2.7 is fast enough: $ PYTHONHASHSEED=0 python2.7 -m timeit -s "import cPickle as pickle, numpy; d=numpy.add" "pickle.dumps(d)" 100000 loops, best of 3: 6.12 usec per loop $ PYTHONHASHSEED=0 python2.7 -m timeit -s "import cPickle as pickle, numpy; d=numpy.diff" "pickle.dumps(d)" 100000 loops, best of 3: 2.35 usec per loop (varying PYTHONHASHSEED didn't produce any slower results) ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <http://bugs.python.org/issue22676> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: https://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
