I\'m looking for a fast way for multiple processes (in multiprocessing.Pool()) to read from a central datasource.Currently I have a file which is read into a queue (using multiprocessing.Manager().Que
I have a multiprocessing code that search database records for a certain condition, when it reaches the end of the database the loop must stop, how can I do that? Here is the code:
I\'m attempting to build a python script that has a pool of worker processes (using mutiprocessing.Pool) across a large set of data.
I\'m developing a simple client-server application in python.I\'m using a manager to set up shared queues, but I can\'t figure out how to pass an arbitrary object from the server to the client.I suspe
I\'m wondering about the way that python\'s Multiprocessing.Pool class works with map, imap, and map_async.My particular problem is that I want to map on an iterator that creates memory-heavy objects,
I\'m interested in running a Python program using a computer cluster. I have in the past been using Python MPI interfaces, but due to difficulties in compiling/installing these, I would prefer solutio
I want to parse through a root folder which is entered by the user by using multi threading and multi processing at different versions.But how can I distinguish while I am parsing through a r开发者_St
I am looking for a working example of multiprocessing.Queue after being pointed to it from this question: Python utilizing multiple processors
As I understand there are two types of modules in python (CPython): - the .so (C extension) - the .py
I am writing a library for doing multi-processing (forking) and would like to test it with PHPUnit. So far I came up with following scenario:开发者_如何学运维
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