On Mon, Jan 19, 2009 at 3:50 PM, gopal mishra <gop...@infotechsw.com> wrote: > i know this is not an io - bound problem, i am creating heavy objects in the > process and add these objects in to queue and get that object in my main > program using queue. > you can test the this sample code > import time > from multiprocessing import Process, Queue > > class Data(object): > def __init__(self): > self.y = range(1, 1000000) > > def getdata(queue): > data = Data() > queue.put(data) > > if __name__=='__main__': > t1 = time.time() > d1 = Data() > d2 = Data() > t2 = time.time() > print "without multiProcessing total time:", t2-t1 > #multiProcessing > queue = Queue() > Process(target= getdata, args=(queue, )).start() > Process(target= getdata, args=(queue, )).start() > s1 = queue.get() > s2 = queue.get() > t2 = time.time() > print "multiProcessing total time::", t2-t1
The reason your code above doesn't work as you expect and the multiprocessing part takes longer is because your Data objects are creating a list (a rather large list) of ints. Use xrange instead of range. Here's what I get (using xrange): $ python test.py without multiProcessing total time: 1.50203704834e-05 multiProcessing total time:: 0.116630077362 cheers James -- http://mail.python.org/mailman/listinfo/python-list
