Global variables problem

Jean-Michel Pichavant jeanmichel at sequans.com
Wed Aug 4 11:24:30 CEST 2010


Navkirat Singh wrote:
>
> On 04-Aug-2010, at 9:46 AM, Daniel da Silva wrote:
>
>> Please post approximate code that actually works and displays the 
>> problem.
>>
>> On Tue, Aug 3, 2010 at 9:00 PM, Navkirat Singh <navkirats at gmail.com 
>> <mailto:navkirats at gmail.com>> wrote:
>>
>>     Hey guys,
>>
>>     I am using a multiprocessing program, where the new process is
>>     supposed to change a variable in the main class that it branches
>>     out from. This is somehow not working, following is an
>>     approximate code. Would really appreciate any insight into this
>>     matter:
>>
>>
>>     var = {}
>>
>>     class Something():
>>
>>            def set_var(self):
>>                    global var
>>                    var = somevalue
>>
>>            def get_var(self):
>>                    return var
>>
>>            def newprocess(self):
>>                    self.set_var()
>>
>>            def do_multiprocessing(self):
>>                    while true:
>>                            self.get_var()
>>                            new_process = process(target=newprocess)
>>                            new_process.start()
>>
>>
>>     I am really confused here !
>>
>>     Any help would be awesome : )
>>
>>     Regards,
>>     Nav
>>
>>     --
>>     http://mail.python.org/mailman/listinfo/python-list
>>
>>
>
> This is a working code, streamlined, but it is where the problem is:
>
> from multiprocessing import * 
>
> dicts = 0
> print('global ', dicts)
>
> class WebServer():
> def set_sessionInfo(self):
> global dicts
> dicts = dicts + 1
> def get_sessionInfo(self):
> return dicts
>
> def handle_connection(self):
> self.set_sessionInfo()
> def serve_forever(self):
> for x in range(10):
> p = Process(target=self.handle_connection)
> p.start()
> print(self.get_sessionInfo())
> ws = WebServer()
> ws.serve_forever()
> print(dicts)
>
>

"As mentioned above, when doing concurrent programming it is usually 
best to avoid using shared state as far as possible. This is 
particularly true when using multiple processes.

However, if you really do need to use some shared data then 
multiprocessing provides a couple of ways of doing so."

source :

http://docs.python.org/library/multiprocessing.html


Read it, everything is explained with examples, including how to 
properly solve your problem.
"When using multiple processes, one generally uses message passing for 
communication between processes and avoids having to use any 
synchronization primitives like locks."

JM



More information about the Python-list mailing list