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Exclusions work on the entire dependency graph below the point where they are declared If you want to exclude Project-E instead of Project-D, simply change the

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I think WMI is the resource to use. Especially, look at the Win32_PerfFormattedData* classes in the MSDN.

A quick search turned this up (among others):

http://timgolden.me.uk/python/wmi.html

The standard approach for huge piles of data amounts to this.

We'll assume that list_1 is the "master" (without duplicates) and list_2 is the "updates" which may have duplicates.

iter_1 = iter( sorted(list_1) ) # Essentially SELECT...ORDER BY"iter_2 = iter( sorted(list_2) )"eof_1 = False"eof_2 = False"try:"    item_1 = iter_1.next()"except StopIteration:"    eof_1= True"try:"    item_2 = iter_2.next()"except StopIteration:"    eof_2= True"while not eof_1 and not eof_2:"    if item_1 == item_2:"        # do your update to create the new master list."        try:"            item_2 = iter_2.next()"        except StopIteration:"            eof_2= True"    elif item_1 < item_2:"        try:"            item_1 = iter_1.next()"        except StopIteration:"            eof_1= True"    elif item_2 < item_1:"        # Do your insert to create the new master list."        try:"            item_2 = iter_2.next()"        except StopIteration:"            eof_2= True"assert eof_1 or eof_2"if eof_1:"    # item_2 and the rest of list_2 are inserts."elif eof_2:"    pass"else:"    raise Error("What!?!?") 

Yes, it involves a potential sort. If list_1 is kept in sorted order when you write it back to the file system, that saves considerable time. If list_2 can be accumulated in a structure that keeps it sorted, then that saves considerable time.

Sorry about the wordiness, but you need to know which iterator raised the StopIteration, so you can't (trivially) wrap the whole while loop in a big-old-try block.

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Just to clarify: the code in the body of a module is not executed every time the module is imported - it is run only once, after which future imports find the already created module, rather than recreating it. Take a look at sys.modules to see the list of cached modules.

However, if your problem is the time it takes for the first import after the program is run, you'll probably need to use some other method than a python dict. Probably best would be to use an on-disk form, for instance a sqlite database, one of the dbm modules.

For a minimal change in your interface, the shelve module may be your best option - this puts a pretty transparent interface between the dbm modules that makes them act like an arbitrary python dict, allowing any picklable value to be stored. Here's an example:

# Create dict with a million items:"import shelve"d = shelve.open('path/to/my_persistant_dict')"d.update(('key%d' % x, x) for x in xrange(1000000))"d.close()

Then in the next process, use it. There should be no large delay, as lookups are only performed for the key requested on the on-disk form, so everything doesn't have to get loaded into memory:

>>> d = shelve.open('path/to/my_persistant_dict')">>> print d['key99999']"99999

It's a bit slower than a real dict, and it will still take a long time to load if you do something that requires all the keys (eg. try to print it), but may solve your problem.

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