Python多進程編程下線程之間變量的共享問題

 　　這篇文章主要介紹了探究Python多進程編程下線程之間變量的共享問題,多進程編程是Python學習進階中的重要知識,需要的朋友可以參考下

　　1、問題：

　　群中有同學貼了如下一段代碼，問為何 list 最後打印的是空值?

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 from multiprocessing import Process, Manager import os   manager = Manager() vip_list = [] #vip_list = manager.list()   def testFunc(cc): vip_list.append(cc) print 'process id:', os.getpid()   if __name__ == '__main__': threads = []   for ll in range(10): t = Process(target=testFunc, args=(ll,)) t.daemon = True threads.append(t)   for i in range(len(threads)): threads[i].start()   for j in range(len(threads)): threads[j].join()   print "------------------------" print 'process id:', os.getpid() print vip_list

　　其實如果你了解 python 的多線程模型，GIL 問題，然後了解多線程、多進程原理，上述問題不難回答，不過如果你不知道也沒關系，跑一下上面的代碼你就知道是什麼問題了。

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 python aa.py process id: 632 process id: 635 process id: 637 process id: 633 process id: 636 process id: 634 process id: 639 process id: 638 process id: 641 process id: 640 ------------------------ process id: 619 []

　　將第 6 行注釋開啟，你會看到如下結果：

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1 2 3 4 5 6 7 8 9 10 11 12 13 process id: 32074 process id: 32073 process id: 32072 process id: 32078 process id: 32076 process id: 32071 process id: 32077 process id: 32079 process id: 32075 process id: 32080 ------------------------ process id: 32066 [3, 2, 1, 7, 5, 0, 6, 8, 4, 9]

　　2、python 多進程共享變量的幾種方式:

　　(1)Shared memory：

　　Data can be stored in a shared memory map using Value or Array. For example, the following code

　　http://docs.python.org/2/library/multiprocessing.html#sharing-state-between-processes

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 from multiprocessing import Process, Value, Array   def f(n, a): n.value = 3.1415927 for i in range(len(a)): a[i] = -a[i]   if __name__ == '__main__': num = Value('d', 0.0) arr = Array('i', range(10))   p = Process(target=f, args=(num, arr)) p.start() p.join()   print num.value print arr[:]

　　結果：

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1 2 3.1415927 [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]

　　(2)Server process：

　　A manager object returned by Manager() controls a server process which holds Python objects and allows other processes to manipulate them using proxies.

　　A manager returned by Manager() will support types list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Queue, Value and Array.

　　代碼見開頭的例子。

　　http://docs.python.org/2/library/multiprocessing.html#managers

　　3、多進程的問題遠不止這麼多：數據的同步

　　看段簡單的代碼：一個簡單的計數器：

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 from multiprocessing import Process, Manager import os   manager = Manager() sum = manager.Value('tmp', 0)   def testFunc(cc): sum.value += cc   if __name__ == '__main__': threads = []   for ll in range(100): t = Process(target=testFunc, args=(1,)) t.daemon = True threads.append(t)   for i in range(len(threads)): threads[i].start()   for j in range(len(threads)): threads[j].join()   print "------------------------" print 'process id:', os.getpid() print sum.value

　　結果：

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1 2 3 ------------------------ process id: 17378 97

　　也許你會問：WTF?其實這個問題在多線程時代就存在了，只是在多進程時代又杯具重演了而已：Lock!

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 from multiprocessing import Process, Manager, Lock import os   lock = Lock() ma