python子类化multiprocessing.Process

Question

我是python面向对象的新手,我将现有的应用程序重写为面向对象的版本,因为现在开发人员正在增加,我的代码变得无法维护.

通常我使用多处理队列,但我从这个例子http://www.doughellmann.com/PyMOTW/multiprocessing/basics.html发现我可以继承,multiprocessing.Process所以我认为这是一个好主意,我写了一个类来测试这样:

码:

from multiprocessing import Process
class Processor(Process):
    def return_name(self):
        return "Process %s" % self.name
    def run(self):
        return self.return_name()

processes = []


if __name__ == "__main__":

        for i in range(0,5):
                p=Processor()
                processes.append(p)
                p.start()
        for p in processes:
                p.join()

但是我无法取回值,我怎样才能以这种方式使用队列？

编辑:我想获得返回值并思考放在哪里Queues().

Answer 1

子类multiprocessing.Process:

但是我无法取回值,我怎样才能以这种方式使用队列？

进程需要a Queue()来接收结果......如何子类的示例multiprocessing.Process如下...

from multiprocessing import Process, Queue
class Processor(Process):

    def __init__(self, queue, idx, **kwargs):
        super(Processor, self).__init__()
        self.queue = queue
        self.idx = idx
        self.kwargs = kwargs

    def run(self):
        """Build some CPU-intensive tasks to run via multiprocessing here."""
        hash(self.kwargs) # Shameless usage of CPU for no gain...

        ## Return some information back through multiprocessing.Queue
        ## NOTE: self.name is an attribute of multiprocessing.Process
        self.queue.put("Process idx={0} is called '{1}'".format(self.idx, self.name))

if __name__ == "__main__":
    NUMBER_OF_PROCESSES = 5

    ## Create a list to hold running Processor object instances...
    processes = list()

    q = Queue()  # Build a single queue to send to all process objects...
    for i in range(0, NUMBER_OF_PROCESSES):
        p=Processor(queue=q, idx=i)
        p.start()
        processes.append(p)

    # Incorporating ideas from this answer, below...
    #    https://stackoverflow.com/a/42137966/667301
    [proc.join() for proc in processes]
    while not q.empty():
        print "RESULT: {0}".format(q.get())   # get results from the queue...

在我的机器上,这导致......

$ python test.py
RESULT: Process idx=0 is called 'Processor-1'
RESULT: Process idx=4 is called 'Processor-5'
RESULT: Process idx=3 is called 'Processor-4'
RESULT: Process idx=1 is called 'Processor-2'
RESULT: Process idx=2 is called 'Processor-3'
$

使用multiprocessing.Pool:

FWIW,我发现子类化的一个缺点multiprocessing.Process是你无法利用所有内置的优点multiprocessing.Pool; Pool如果您不需要生产者和消费者代码通过队列相互通信,那么会为您提供一个非常好的API .

您可以使用一些创意返回值做很多事情......在下面的示例中,我使用a dict()封装来自pool_job()...的输入和输出值

from multiprocessing import Pool

def pool_job(input_val=0):
    # FYI, multiprocessing.Pool can't guarantee that it keeps inputs ordered correctly
    # dict format is {input: output}...
    return {'pool_job(input_val={0})'.format(input_val): int(input_val)*12}

pool = Pool(5)  # Use 5 multiprocessing processes to handle jobs...
results = pool.map(pool_job, xrange(0, 12)) # map xrange(0, 12) into pool_job()
print results

这导致:

[
    {'pool_job(input_val=0)': 0}, 
    {'pool_job(input_val=1)': 12}, 
    {'pool_job(input_val=2)': 24}, 
    {'pool_job(input_val=3)': 36}, 
    {'pool_job(input_val=4)': 48}, 
    {'pool_job(input_val=5)': 60}, 
    {'pool_job(input_val=6)': 72}, 
    {'pool_job(input_val=7)': 84}, 
    {'pool_job(input_val=8)': 96}, 
    {'pool_job(input_val=9)': 108}, 
    {'pool_job(input_val=10)': 120}, 
    {'pool_job(input_val=11)': 132}
]

显然,还有许多其他改进pool_job(),例如错误处理,但这说明了基本要素.仅供参考,这个答案提供了另一个如何使用的例子multiprocessing.Pool.