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The essential idea of a threaded or multi-process server is that we take the simple and straightforward server that we started out with the one way back in Listing 7 2, the one that waits repeatedly on a
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single client and then sends back the information it needs and run several copies of it at once so that we can serve several clients at once, without making them wait on each other. The event-driven approaches in Listings 7 7 and 7 8 place upon our own program the burden of figuring out which client is ready next, and how to interleave requests and responses depending on the order in which they arrive. But when using threads and processes, you get to transfer this burden to the operating system itself. Each thread controls one client socket; it can use blocking recv() and send() calls to wait until data can be received and transmitted; and the operating system then decides which workers to leave idle and which to wake up. Using multiple threads or processes is very common, especially in high-capacity web and database servers. The Apache web server even comes with both: its prefork module offers a pool of processes, while the worker module runs multiple threads instead. Listing 7 9 shows a simple server that creates multiple workers. Note how pleasantly symmetrical the Standard Library authors have made the interface between threads and processes, thanks especially to Jesse Noller and his recent work on the multiprocessing module. The main program logic does not even know which solution is being used; the two classes have a similar enough interface that either Thread or Process can here be used interchangeably. Listing 7 9. Multi-threaded or Multi-process Server #!/usr/bin/env python # Foundations of Python Network Programming - 7 - server_multi.py # Using multiple threads or processes to serve several clients in parallel. import sys, time, launcelot from multiprocessing import Process from server_simple import server_loop from threading import Thread WORKER_CLASSES = {'thread': Thread, 'process': Process} WORKER_MAX = 10 def start_worker(Worker, listen_sock): worker = Worker(target=server_loop, args=(listen_sock,)) worker.daemon = True # exit when the main process does worker.start() return worker
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if __name__ == '__main__': if len(sys.argv) != 3 or sys.argv[2] not in WORKER_CLASSES: print >>sys.stderr, 'usage: server_multi.py interface thread|process' sys.exit(2) Worker = WORKER_CLASSES[sys.argv.pop()] # setup() wants len(argv)==2 # Every worker will accept() forever on the same listening socket. listen_sock = launcelot.setup() workers = [] for i in range(WORKER_MAX): workers.append(start_worker(Worker, listen_sock)) # Check every two seconds for dead workers, and replace them. while True: time.sleep(2)
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worker in workers: if not worker.is_alive(): print worker.name, "died; starting replacement" workers.remove(worker) workers.append(start_worker(Worker, listen_sock))
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First, notice how this server is able to re-use the simple, procedural approach to answering client requests that it imports from the launcelot.py file we introduced in Listing 7 2. Because the operating system keeps our threads or processes separate, they do not have to be written with any awareness that other workers might be operating at the same time. Second, note how much work the operating system is doing for us! It is letting multiple threads or processes all call accept() on the very same server socket, and instead of raising an error and insisting that only one thread at a time be able to wait for an incoming connection, the operating system patiently queues up all of our waiting workers and then wakes up one worker for each new connection that arrives. The fact that a listening socket can be shared at all between threads and processes, and that the operating system does round-robin balancing among the workers that are waiting on an accept() call, is one of the great glories of the POSIX network stack and execution model; it makes programs like this very simple to write. Third, although I chose not to complicate this listing with error-handling or logging code any exceptions encountered in a thread or process will be printed as tracebacks directly to the screen I did at least throw in a loop in the master thread that checks the health of the workers every few seconds, and starts up replacement workers for any that have failed. Figure 7 4 shows the result of our efforts: performance that is far above that of the single-threaded server, and that also beats slightly both of the event-driven servers we looked at earlier.
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Figure 7 4. Multi-process server benchmark Again, given the limitations of my small duo-core laptop, the server starts falling away from linear behavior as the load increases from 5 to 10 simultaneous clients, and by the time it reaches 15 concurrent users, the number of 10-question request sequences that it can answer every second has fallen from around 70 per client to less than 50. And then as will be familiar to anyone who has studied
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