Python - 队列，管理器和多处理的奇怪行为

Question

在使用multiprocessing模块同时下载我的宠物项目时，我遇到了一个涉及由multiprocessing.Manager()对象生成的Queue对象的奇怪行为。

根据我将Queue对象（通过Manager生成）放在另一个Queue对象（也是通过Manager生成）中的方式，我得到了一个不同的行为，据我理解，同样的事情。 这是一个最小的工作示例：

import multiprocessing
import Queue

def work(inbound_queue, keep_going):
    while keep_going.value == 1:
        try:
            outbound_queue = inbound_queue.get(False) # this fails in case 3
            #do some work
            outbound_queue.put("work done!")
        except Queue.Empty:
            pass #this is busy wait of course, it's just an example

class Weird:
    def __init__(self):
        self.manager = multiprocessing.Manager()
        self.queue = self.manager.Queue()
        self.keep_going = multiprocessing.Value("i", 1)
        self.worker = multiprocessing.Process(target = work, args = (self.queue, self.keep_going))
        self.worker.start()
    def stop(self): #close and join the second process
        self.keep_going.value = 0
        self.worker.join()
    def queueFromOutside(self, q):
        self.queue.put(q)
        return q
    def queueFromNewManager(self):
        q = multiprocessing.Manager().Queue()
        self.queue.put(q)
        return q
    def queueFromOwnManager(self):
        q = self.manager.Queue()
        self.queue.put(q)
        return q

if __name__ == '__main__':
    instance = Weird()
    # CASE 1
    queue = multiprocessing.Manager().Queue()
    q1 = instance.queueFromOutside(queue) # Works fine
    print "1: ", q1.get()

    # CASE 2
    q2 = instance.queueFromNewManager()   # Works fine
    print "2: ", q2.get()

    # CASE 3
    q3 = instance.queueFromOwnManager()   # Error
    print "3: ", q3.get()

    instance.stop() #sadly never called :(

及其输出（python 2.7.10 x86，windows）。

主要输出：

1:  work done!
2:  work done!
3:

然后工作进程崩溃，让q3.get（）挂起。

工作流程的输出：

Process Process-2:
Traceback (most recent call last):
  File "C:\Python27\lib\multiprocessing\process.py", line 258, in _bootstrap
    self.run()
  File "C:\Python27\lib\multiprocessing\process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "J:\Dropbox\Python\queues2.py", line 7, in work
    outbound_queue = inbound_queue.get(False) # this fails in case 3
  File "<string>", line 2, in get
  File "C:\Python27\lib\multiprocessing\managers.py", line 774, in _callmethod
    raise convert_to_error(kind, result)
RemoteError:
---------------------------------------------------------------------------
Unserializable message: ('#RETURN', <Queue.Queue instance at 0x025A22B0>)
---------------------------------------------------------------------------

所以问题是：为什么第三种情况导致RemoteError ？

提供的示例与实际项目中的代码结构不相似，但我确实将队列发送到正在运行的进程，如果我使用方法＃1和＃2进行处理，它的工作正常。 但是使用方法＃3会很好，因为它可以省去每次获取Manager的麻烦，这可能会花费很长的时间（我现在正在使用的机器上大约100毫秒）。

这个问题出于好奇，因为我还在学习multiprocessing模块中所有很酷的东西。

更新，尝试澄清问题：在案例3（ queueFromOwnManager ）中，为什么self.manager.Queue()创建一个曾经放入self.queue的队列，在创建队列时无法使用self.queue.get()检索with multiprocessing.Manager().Queue()可以检索multiprocessing.Manager().Queue()吗？ 执行3个案件的顺序无关紧要。 理想情况下，在3个示例中的3个方法调用中的任何一个之前和之后， instance.queue将为空。

更新2：使得exaple更像我在代码中实际做的那样

Answer 1

更新了答案，我在main中添加了代码，用于从列表ls中填充和打印放置在队列self.queue中的项目。 我还添加了一个语句，可用于从外部函数中的ls中检索self.queue中的项目。

import multiprocessing
import Queue

def work(inbound_queue, keep_going):
    while keep_going.value == 1:
        try:
            pass
            #outbound_queue = inbound_queue.get(False) # this fails in case 3 #<--- an error here, wherefrom does it get truthvalues?
            #do some work
            #outbound_queue.put("work done!")
        except: #Queue.Empty:                            <--- self.queue.Empty(): when instantiated below
            pass #this is busy wait of course, it's just an example

class Weird:
    def __init__(self):
        self.manager = multiprocessing.Manager()
        self.queue = self.manager.Queue()
        self.queue2 = multiprocessing.Manager().Queue()
        self.keep_going = multiprocessing.Value("i", 1)
        self.worker = multiprocessing.Process(target = work, args = (self.queue, self.keep_going))
        self.worker.start()
    def stop(self): #close and join the second process
        self.keep_going.value = 0
        #self.worker.join()
    def queueFromOutside(self, q):

        ls = [1,2,3,4,5]
        # populate self.queue with elements from list ls
        for i in ls:
          self.queue.put(i)
        return self.queue
    def queueFromNewManager(self):
         #q = multiprocessing.Manager().Queue()  <---- note that you state that manager and not "queue"
                                                      # is the name of the queue in this step,
                                                      # therefor errormsg, at this step manager
                                                     # is empty and self.queue has been given ls


        ls = [5,6,7,8]
        # populate self.queue with elements from list ls
        for i in ls:

          self.queue.put(i)
        return self.queue


    def queueFromOwnManager(self):
        q = self.manager.Queue()


        ls = [5,6,7,8]
        # populate self.queue with elements from list ls
        for i in ls:
          self.queue.put(i)
        return self.queue

    def wait_completion(self):     #<---- function that waits until tasks are done, and joins all
                                            # tasks as a last step, check docs how to add tasks and data to manager
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

if __name__ == '__main__':
    instance = Weird()
    # CASE 1

    q1 = instance.queueFromOutside(instance.queue2) # Works fine
    print "1: ", q1.get()

    #this code gets data from instance.queue in external functions
    if not instance.queue.empty():

        item = instance.queue.get(True)
        print item,"item"

    # CASE 2
    q2 = instance.queueFromNewManager()   # Works fine
    print "2: ", q2.get()

    # CASE 3
    q3 = instance.queueFromOwnManager()   # Error
    print "3: ", q3.get()

    instance.stop() #sadly never called :(

    # when all tasks done  wait_completion(self) can be printed here to join all tasks