1、問題：
羣中有同學貼瞭如下一段代碼，問爲何 list 最後打印的是空值？
from multiprocessing import Process, Managerimport 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
其實如果你瞭解 python 的多線程模型，GIL 問題，然後瞭解多線程、多進程原理，上述問題不難回答，不過如果你不知道也沒關係，跑一下上面的代碼你就知道是什麼問題了。
python aa.pyprocess id: 632process id: 635process id: 637process id: 633process id: 636process id: 634process id: 639process id: 638process id: 641process id: 640------------------------process id: 619[]
將第 6 行註釋開啓，你會看到如下結果：
process id: 32074process id: 32073process id: 32072process id: 32078process id: 32076process id: 32071process id: 32077process id: 32079process id: 32075process 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
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：
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：
from multiprocessing import Process, Managerimport 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：
------------------------process id: 1737897
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！
from multiprocessing import Process, Manager, Lockimport os lock = Lock()manager = Manager()sum = manager.Value('tmp', 0) def testFunc(cc, lock): with lock: sum.value += cc if __name__ == '__main__': threads = [] for ll in range(100): t = Process(target=testFunc, args=(1, lock)) 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
這段代碼性能如何呢？跑跑看，或者加大循環次數試一下。。。
再來看個多進程共享變量的例子：該腳本可以在集羣中批量執行任意命令並返回結果。
#!/usr/bin/env python# coding=utf-8import sys reload(sys)sys.setdefaultencoding('utf-8') import rpycfrom pyUtil import *from multiprocessing import Pool as ProcessPoolfrom multiprocessing import Manager hostDict = { '192.168.1.10': 11111} manager = Manager()localResultDict = manager.dict() def rpc_client(host_port_cmd): host = host_port_cmd[0] port = host_port_cmd[1] cmd = host_port_cmd[2] c = rpyc.connect(host, port) result = c.root.exposed_execCmd(cmd) localResultDict[host] = result c.close() def exec_cmd(cmd_str): host_port_list = [] for (host, port) in hostDict.items(): host_port_list.append((host, port, cmd_str)) pool = ProcessPool(len(hostDict)) results = pool.map(rpc_client, host_port_list) pool.close() pool.join() for ip in localResultDict.keys(): print ip + ":\t" + localResultDict[ip] if __name__ == "__main__": if len(sys.argv) == 2 and sys.argv[1] != "-h": print "======================" print " Your command is:\t" + sys.argv[1] print "======================" cmd_str = sys.argv[1] else: print "Cmd Error!" sys.exit(1) exec_cmd(cmd_str)
需要注意的是 manager.dict() 在遍歷時一定要使用 .keys() 方法，否則會拋異常：
Traceback (most recent call last): File "test.py", line 83, in <module> exec_cmd(cmd_str) File "test.py", line 57, in exec_cmd for ip in localResultDict: File "<string>", line 2, in __getitem__ File "/opt/soft/python-2.7.10/lib/python2.7/multiprocessing/managers.py", line 774, in _callmethod raise convert_to_error(kind, result)KeyError: 0
4、最後的建議：
Note that usually sharing data between processes may not be the best choice, because of all the synchronization issues; an approach involving actors exchanging messages is usually seen as a better choice. See also Python documentation: As mentioned above, when doing concurrent programming it is usually best to avoid using shared state as far as possible. This is particularly true when using multiple processes. However, if you really do need to use some shared data then multiprocessing provides a couple of ways of doing so.
5、Refer：
[0] 理解Python併發編程一篇就夠了 - 線程篇
[1] multiprocessing — Process-based “threading” interface
https://docs.python.org/2/library/multiprocessing.html
[2] Manager()出錯，不知道爲什麼
http://m.newsmth.net/article/Python/100915
[3] Can't iterate over multiprocessing.managers.DictProxy
http://bugs.python.org/issue9733
轉載於:https://my.oschina.net/leejun2005/blog/203148
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