[I tried googling for this, didn't find anything relevant.] We've recently been doing some profiling on a project of ours. It runs quite fast on Linux but *really* bogs down on Windows 2003. We initially thought it was the simplejson libraries (we don't use the C extensions) but profiling proved otherwise.
We have a function that does some runtime imports via calls to __import__. We ran 1000 iterations (we used cProfile) of the application (web app). There were eight calls to __import__ per iteration, so 8000 calls total. Identical hardware, by the way. On Linux (Debian Etch, Python 2.5.1) Total time was 2.793 CPU seconds, with __import__ using 1.059 seconds of that. So, 37% of the time was spent in import. Not great, but not a show stopper. On Windows 2003 (R2, Python 2.5.1) Total time was 18.532 CPU seconds, with __import__ using 16.330 seconds (88%) of that. So, Linux spends 1.734 seconds on non-import activities, and Windows spends 2.202 seconds on non-import activities. Pretty close. But 16.3 seconds on import!? Is this a known deficiency in Windows' Python import calls, or is there something deeper going on here? Pointers, suggestions, and URLs welcome. j -- http://mail.python.org/mailman/listinfo/python-list
