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    <p>Here's a simple example: you need to try a few alternative URLs and return the contents of the first one to respond.</p> <pre><code>import Queue import threading import urllib2 # called by each thread def get_url(q, url): q.put(urllib2.urlopen(url).read()) theurls = ["http://google.com", "http://yahoo.com"] q = Queue.Queue() for u in theurls: t = threading.Thread(target=get_url, args = (q,u)) t.daemon = True t.start() s = q.get() print s </code></pre> <p>This is a case where threading is used as a simple optimization: each subthread is waiting for a URL to resolve and respond, in order to put its contents on the queue; each thread is a daemon (won't keep the process up if main thread ends -- that's more common than not); the main thread starts all subthreads, does a <code>get</code> on the queue to wait until one of them has done a <code>put</code>, then emits the results and terminates (which takes down any subthreads that might still be running, since they're daemon threads).</p> <p>Proper use of threads in Python is invariably connected to I/O operations (since CPython doesn't use multiple cores to run CPU-bound tasks anyway, the only reason for threading is not blocking the process while there's a wait for some I/O). Queues are almost invariably the best way to farm out work to threads and/or collect the work's results, by the way, and they're intrinsically threadsafe so they save you from worrying about locks, conditions, events, semaphores, and other inter-thread coordination/communication concepts.</p>
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