|An Introduction to Python|
by Guido van Rossum and Fred L. Drake, Jr.
Paperback (6"x9"), 124 pages
RRP £12.95 ($19.95)
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Threading is a technique for decoupling tasks which are not sequentially dependent. Threads can be used to improve the responsiveness of applications that accept user input while other tasks run in the background. A related use case is running I/O in parallel with computations in another thread.
The following code shows how the high level ‘threading’ module can run tasks in background while the main program continues to run:
import threading, zipfile class AsyncZip(threading.Thread): def __init__(self, infile, outfile): threading.Thread.__init__(self) self.infile = infile self.outfile = outfile def run(self): f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED) f.write(self.infile) f.close() print 'Finished background zip of: ', self.infile background = AsyncZip('mydata.txt', 'myarchive.zip') background.start() print 'The main program continues to run in foreground.' background.join() # Wait for background task to finish print 'Main program waited until background was done.'
The principal challenge of multi-threaded applications is coordinating threads that share data or other resources. To that end, the threading module provides a number of synchronization primitives including locks, events, condition variables, and semaphores.
While those tools are powerful, minor design errors can result in
problems that are difficult to reproduce. So, the preferred approach
to task coordination is to concentrate all access to a resource
in a single thread and then use the
‘Queue’ module to feed that
thread with requests from other threads. Applications using
Queue objects for inter-thread communication and coordination
are easier to design, more readable, and more reliable.
|ISBN 0954161769||An Introduction to Python||See the print edition|