Pipe()只能有两个端点。

Queue()可以有多个生产者和消费者。

何时使用它们

如果您需要两个以上的点进行通信，请使用Queue() 。

如果你需要绝对性能， Pipe()要快得多，因为Queue()是建立在Pipe()之上的。

绩效基准

假设您想要生成两个进程并尽快在它们之间发送消息。 这些是使用Pipe()和Queue()类似测试之间的拖拽竞赛的时间结果......这是在运行Ubuntu 11.10和Python 2.7.2的ThinkpadT61上。

仅供参考，我将JoinableQueue()结果作为奖励投入; JoinableQueue()在queue.task_done()时会queue.task_done()任务(它甚至不知道特定任务，它只计算队列中未完成的任务)，因此queue.join()知道工作已完成。

这个答案底部的每个代码......

mpenning@mpenning-T61:~$ python multi_pipe.py

Sending 10000 numbers to Pipe() took 0.0369849205017 seconds

Sending 100000 numbers to Pipe() took 0.328398942947 seconds

Sending 1000000 numbers to Pipe() took 3.17266988754 seconds

mpenning@mpenning-T61:~$ python multi_queue.py

Sending 10000 numbers to Queue() took 0.105256080627 seconds

Sending 100000 numbers to Queue() took 0.980564117432 seconds

Sending 1000000 numbers to Queue() took 10.1611330509 seconds

mpnening@mpenning-T61:~$ python multi_joinablequeue.py

Sending 10000 numbers to JoinableQueue() took 0.172781944275 seconds

Sending 100000 numbers to JoinableQueue() took 1.5714070797 seconds

Sending 1000000 numbers to JoinableQueue() took 15.8527247906 seconds

mpenning@mpenning-T61:~$

总之， Pipe()比Queue()快三倍。 除非你真的必须有好处，否则不要考虑JoinableQueue() 。

奖金材料2

多处理引入了信息流的细微变化，除非您知道一些快捷方式，否则会使调试变得困难。 例如，在许多条件下通过字典索引时，您可能有一个可正常工作的脚本，但在某些输入中很少失败。

通常我们会在整个python进程崩溃时找到失败的线索; 但是，如果多处理功能崩溃，则不会将未经请求的崩溃回溯打印到控制台。 追踪未知的多处理崩溃是很困难的，没有一个线索来解决崩溃的过程。

我发现追踪多处理崩溃信息的最简单方法是将整个多处理函数包装在try / except并使用traceback.print_exc() ：

import traceback

def reader(args):

try:

# Insert stuff to be multiprocessed here

return args[0]['that']

except:

print "FATAL: reader({0}) exited while multiprocessing".format(args)

traceback.print_exc()

现在，当您发现崩溃时，您会看到以下内容：

FATAL: reader([{'crash', 'this'}]) exited while multiprocessing

Traceback (most recent call last):

File "foo.py", line 19, in __init__

self.run(task_q, result_q)

File "foo.py", line 46, in run

raise ValueError

ValueError

源代码：

"""

multi_pipe.py

"""

from multiprocessing import Process, Pipe

import time

def reader_proc(pipe):

## Read from the pipe; this will be spawned as a separate Process

p_output, p_input = pipe

p_input.close() # We are only reading

while True:

msg = p_output.recv() # Read from the output pipe and do nothing

if msg=='DONE':

break

def writer(count, p_input):

for ii in xrange(0, count):

p_input.send(ii) # Write 'count' numbers into the input pipe

p_input.send('DONE')

if __name__=='__main__':

for count in [10**4, 10**5, 10**6]:

# Pipes are unidirectional with two endpoints: p_input ------> p_output

p_output, p_input = Pipe() # writer() writes to p_input from _this_ process

reader_p = Process(target=reader_proc, args=((p_output, p_input),))

reader_p.daemon = True

reader_p.start() # Launch the reader process

p_output.close() # We no longer need this part of the Pipe()

_start = time.time()

writer(count, p_input) # Send a lot of stuff to reader_proc()

p_input.close()

reader_p.join()

print("Sending {0} numbers to Pipe() took {1} seconds".format(count,

(time.time() - _start)))

"""

multi_queue.py

"""

from multiprocessing import Process, Queue

import time

import sys

def reader_proc(queue):

## Read from the queue; this will be spawned as a separate Process

while True:

msg = queue.get() # Read from the queue and do nothing

if (msg == 'DONE'):

break

def writer(count, queue):

## Write to the queue

for ii in range(0, count):

queue.put(ii) # Write 'count' numbers into the queue

queue.put('DONE')

if __name__=='__main__':

pqueue = Queue() # writer() writes to pqueue from _this_ process

for count in [10**4, 10**5, 10**6]:

### reader_proc() reads from pqueue as a separate process

reader_p = Process(target=reader_proc, args=((pqueue),))

reader_p.daemon = True

reader_p.start() # Launch reader_proc() as a separate python process

_start = time.time()

writer(count, pqueue) # Send a lot of stuff to reader()

reader_p.join() # Wait for the reader to finish

print("Sending {0} numbers to Queue() took {1} seconds".format(count,

(time.time() - _start)))

"""

multi_joinablequeue.py

"""

from multiprocessing import Process, JoinableQueue

import time

def reader_proc(queue):

## Read from the queue; this will be spawned as a separate Process

while True:

msg = queue.get() # Read from the queue and do nothing

queue.task_done()

def writer(count, queue):

for ii in xrange(0, count):

queue.put(ii) # Write 'count' numbers into the queue

if __name__=='__main__':

for count in [10**4, 10**5, 10**6]:

jqueue = JoinableQueue() # writer() writes to jqueue from _this_ process

# reader_proc() reads from jqueue as a different process...

reader_p = Process(target=reader_proc, args=((jqueue),))

reader_p.daemon = True

reader_p.start() # Launch the reader process

_start = time.time()

writer(count, jqueue) # Send a lot of stuff to reader_proc() (in different process)

jqueue.join() # Wait for the reader to finish

print("Sending {0} numbers to JoinableQueue() took {1} seconds".format(count,

(time.time() - _start)))