I was recently working on improving the efficiency of my botnet analysis code by utilizing 100% of the CPU resources available to my machine. In order to do that in python I needed to span multiple processes as multithreading would produce no benefit for these CPU bound events. While python utilizes true threads that have the ability to run on different cores concurrently, the Global Interpreter Lock, or GIL, makes it such that only one of these threads can run “concurrently”. Thus the simplest solution seemed to be utilizing python’s multiprocessing module.
Python’s multiprocessing module is actually quite simple to use, especially if you’ve previously used python’s threading module. Additionally the multiprocessing module contains a pool class which automatically sets up processes to manage a pool of jobs. There is, however, one HUGE caveat. The pool of workers cannot be terminated until all the tasks have been consumed. After some simple experimentation I noticed two key things with the multiprocessing.pool feature. First, while the worker processes can handle the KeyboardInterrupt and call sys.exit, these processes persist and thus receive future tasks. Second, the KeyboardInterrupt is not delivered to the parent process until all jobs are completed.
#!/usr/bin/env python import multiprocessing, os, time def do_work(): print 'Work Started: %d' % os.getpid() time.sleep(2) return 'Success' def pool_function(): try: return do_work() except KeyboardInterrupt: return 'KeyboardException' def main(): pool = multiprocessing.Pool(3) try: jobs =  for i in range(6): jobs.append(pool.apply_async(pool_function, args=())) pool.close() pool.join() except KeyboardInterrupt: print 'parent received control-c' pool.terminate() for i in jobs: if i.successful(): print i.get() else: print 'Job failed: %s %s' % (type(i._value), i._value) if __name__ == "__main__": main()
I constructed a fairly simple example of this behavior, shown above. Running the code will span three worker processes to handle a total of six jobs. The job is very simple: display a message, sleep for two seconds and return a message to the parent. You’ll notice that when you send a KeyboardInterrupt by pressing ctrl+c (on Linux) it’ll kill the currently running child processes, however the next job will simply take its place until there are no remaining jobs.
#!/usr/bin/env python import multiprocessing, os, signal, time, Queue def do_work(): print 'Work Started: %d' % os.getpid() time.sleep(2) return 'Success' def manual_function(job_queue, result_queue): signal.signal(signal.SIGINT, signal.SIG_IGN) while not job_queue.empty(): try: job = job_queue.get(block=False) result_queue.put(do_work()) except Queue.Empty: pass #except KeyboardInterrupt: pass def main(): job_queue = multiprocessing.Queue() result_queue = multiprocessing.Queue() for i in range(6): job_queue.put(None) workers =  for i in range(3): tmp = multiprocessing.Process(target=manual_function, args=(job_queue, result_queue)) tmp.start() workers.append(tmp) try: for worker in workers: worker.join() except KeyboardInterrupt: print 'parent received ctrl-c' for worker in workers: worker.terminate() worker.join() while not result_queue.empty(): print result_queue.get(block=False) if __name__ == "__main__": main()
In order to behave as I would expect it to, I had to stop using the convenient pool feature and thus I wrote the pool handling code myself as displayed above. By running the code, you’ll notice that I had to implement a job_queue and result_queue to pass information between the parent and children processes. More importantly you’ll notice that when you send a KeyboardInterrupt the remaining tasks are not executed and the parent safely exits almost immediately. One final note is that I chose to ignore KeyboardInterrupts in the child completely by using the signal module. This functionality could additionally be accomplished with the except statement commented out on line 17.
On a separate note, I would like to say that this is my 100th blog post. Awesome!
Response to Georges comment on 2011/02/03
In response to Georges comment I wanted to see how such an approach would work in a case similar to what was done above. The below code demonstrates the approach from the stackoverflow response by Glenn Maynard to call the get function with a timeout.
#!/usr/bin/env python import multiprocessing, os, time def do_work(i): try: print 'Work Started: %d %d' % (os.getpid(), i) time.sleep(2) return 'Success' except KeyboardInterrupt, e: pass def main(): pool = multiprocessing.Pool(3) p = pool.map_async(do_work, range(6)) try: results = p.get(0xFFFF) except KeyboardInterrupt: print 'parent received control-c' return for i in results: print i if __name__ == "__main__": main()
This code works exactly as expected (yay!) however there is one difference between this approach and my solution. With this approach, as is, it is not possible to retrieve the results which completed prior to the keyboard interrupt. If that is not important, than this is a much more elegant solution. Thanks for bringing the solution to my attention Georges.