如何在Python中使用多处理队列？

Question

我在尝试理解多处理队列如何在python上运行以及如何实现它时遇到了很多麻烦。假设我有两个从共享文件访问数据的python模块，让我们将这两个模块称为编写者和读者。我的计划是让读者和写者将请求分成两个独立的多处理队列，然后让第三个进程在循环中弹出这些请求并按原样执行。

我的主要问题是我真的不知道如何正确实现multiprocessing.queue，你不能真正实例化每个进程的对象，因为它们将是独立的队列，你如何确保所有进程都与共享相关队列（或者在这种情况下，队列）

Answer 1

  

我的主要问题是我真的不知道如何正确实现multiprocessing.queue，你不能真正实例化每个进程的对象，因为它们将是独立的队列，你如何确保所有进程都与共享队列（或者在这种情况下，队列）

这是一个简单的读写器共享单个队列的例子......作者向读者发送了一堆整数;当作者用完数字时，它会发送“DONE＆＃39;”，让读者知道如何摆脱读取循环。

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)))

Answer 2

在“from queue import Queue”中没有名为queue的模块，而应使用multiprocessing。因此，它应该看起来像“from multiprocessing import Queue”

Answer 3

在尝试建立一种使用队列传递大熊猫数据帧的多重处理方式时，我看了堆栈溢出和网络上的多个答案。在我看来，每个答案都在重复相同的解决方案，而没有考虑众多极端情况，在进行此类计算时肯定会遇到这种情况。问题在于同时有很多事情在发生。任务数，工作人员数，每个任务的持续时间以及任务执行期间可能出现的异常。所有这些都使同步变得棘手，大多数答案都无法解决您如何进行同步。所以这是我经过几个小时的学习后所希望的，希望这对于大多数人来说足够通用，以至于觉得有用。

任何编码示例之前的一些想法。由于queue.Empty或queue.qsize()或任何其他类似方法对于流量控制都不可靠，因此类似的任何代码

while True:
    try:
        task = pending_queue.get_nowait()
    except queue.Empty:
        break

是假的。即使几毫秒后队列中出现另一个任务，这也会杀死该工作程序。工作人员将无法恢复，一段时间后，所有工作人员都将消失，因为他们随机发现队列暂时空了。最终结果将是在没有完成所有任务的情况下返回主多处理函数（在进程上具有join（）的函数）。真好如果您有成千上万的任务，而有一些任务缺失，那么请通过调试来进行好运。

另一个问题是哨兵值的使用。许多人建议在队列中添加一个哨兵值以标记队列的结束。但是要确切地标记给谁？如果有N个工作程序，则假设N是可用的给定或获取的内核数，则单个标记值将仅将队列结束标记为一个工作程序。当剩下的工人都剩无余时，其他所有工人将坐在那里等待更多的工作。我见过的典型例子是

while True:
    task = pending_queue.get()
    if task == SOME_SENTINEL_VALUE:
        break

一名工人将获得前哨值，其余工人将无限期等待。我没有碰到任何帖子提到您需要将最少数量的哨兵值提交给队列，以便所有人都能得到。

另一个问题是任务执行期间的异常处理。同样，这些应该被捕获和管理。此外，如果您有completed_tasks队列，则在确定完成工作之前，应以确定性方式独立计算队列中有多少项。再次依赖队列大小注定会失败并返回意外结果。

在下面的示例中，par_proc()函数将收到任务列表，包括将与这些任务一起使用的函数以及任何命名的参数和值。

import multiprocessing as mp
import dill as pickle
import queue
import time
import psutil

SENTINEL = None


def do_work(tasks_pending, tasks_completed):
    # Get the current worker's name
    worker_name = mp.current_process().name

    while True:
        try:
            task = tasks_pending.get_nowait()
        except queue.Empty:
            print(worker_name + ' found an empty queue. Sleeping for a while before checking again...')
            time.sleep(0.01)
        else:
            try:
                if task == SENTINEL:
                    print(worker_name + ' no more work left to be done. Exiting...')
                    break

                print(worker_name + ' received some work... ')
                time_start = time.perf_counter()
                work_func = pickle.loads(task['func'])
                result = work_func(**task['task'])
                tasks_completed.put({work_func.__name__: result})
                time_end = time.perf_counter() - time_start
                print(worker_name + ' done in {} seconds'.format(round(time_end, 5)))
            except Exception as e:
                print(worker_name + ' task failed. ' + str(e))
                tasks_completed.put({work_func.__name__: None})


def par_proc(job_list, num_cpus=None):

    # Get the number of cores
    if not num_cpus:
        num_cpus = psutil.cpu_count(logical=False)

    print('* Parallel processing')
    print('* Running on {} cores'.format(num_cpus))

    # Set-up the queues for sending and receiving data to/from the workers
    tasks_pending = mp.Queue()
    tasks_completed = mp.Queue()

    # Gather processes and results here
    processes = []
    results = []

    # Count tasks
    num_tasks = 0

    # Add the tasks to the queue
    for job in job_list:
        for task in job['tasks']:
            expanded_job = {}
            num_tasks = num_tasks + 1
            expanded_job.update({'func': pickle.dumps(job['func'])})
            expanded_job.update({'task': task})
            tasks_pending.put(expanded_job)

    # Use as many workers as there are cores (usually chokes the system so better use less)
    num_workers = num_cpus

    # We need as many sentinels as there are worker processes so that ALL processes exit when there is no more
    # work left to be done.
    for c in range(num_workers):
        tasks_pending.put(SENTINEL)

    print('* Number of tasks: {}'.format(num_tasks))

    # Set-up and start the workers
    for c in range(num_workers):
        p = mp.Process(target=do_work, args=(tasks_pending, tasks_completed))
        p.name = 'worker' + str(c)
        processes.append(p)
        p.start()

    # Gather the results
    completed_tasks_counter = 0
    while completed_tasks_counter < num_tasks:
        results.append(tasks_completed.get())
        completed_tasks_counter = completed_tasks_counter + 1

    for p in processes:
        p.join()

    return results

这是对上面的代码运行的测试

def test_parallel_processing():
    def heavy_duty1(arg1, arg2, arg3):
        return arg1 + arg2 + arg3

    def heavy_duty2(arg1, arg2, arg3):
        return arg1 * arg2 * arg3

    task_list = [
        {'func': heavy_duty1, 'tasks': [{'arg1': 1, 'arg2': 2, 'arg3': 3}, {'arg1': 1, 'arg2': 3, 'arg3': 5}]},
        {'func': heavy_duty2, 'tasks': [{'arg1': 1, 'arg2': 2, 'arg3': 3}, {'arg1': 1, 'arg2': 3, 'arg3': 5}]},
    ]

    results = par_proc(task_list)

    job1 = sum([y for x in results if 'heavy_duty1' in x.keys() for y in list(x.values())])
    job2 = sum([y for x in results if 'heavy_duty2' in x.keys() for y in list(x.values())])

    assert job1 == 15
    assert job2 == 21

再加上一个例外

def test_parallel_processing_exceptions():
    def heavy_duty1_raises(arg1, arg2, arg3):
        raise ValueError('Exception raised')
        return arg1 + arg2 + arg3

    def heavy_duty2(arg1, arg2, arg3):
        return arg1 * arg2 * arg3

    task_list = [
        {'func': heavy_duty1_raises, 'tasks': [{'arg1': 1, 'arg2': 2, 'arg3': 3}, {'arg1': 1, 'arg2': 3, 'arg3': 5}]},
        {'func': heavy_duty2, 'tasks': [{'arg1': 1, 'arg2': 2, 'arg3': 3}, {'arg1': 1, 'arg2': 3, 'arg3': 5}]},
    ]

    results = par_proc(task_list)

    job1 = sum([y for x in results if 'heavy_duty1' in x.keys() for y in list(x.values())])
    job2 = sum([y for x in results if 'heavy_duty2' in x.keys() for y in list(x.values())])

    assert not job1
    assert job2 == 21

希望有帮助。

Answer 4

我们实现了两个版本，一个是简单的多线程池，可以执行多种类型的可调用对象，从而使我们的生活更加轻松；第二个版本使用了进程 ，这在可调用性方面较不灵活，需要和额外调用莳萝。

将Frozen_pool设置为true将冻结执行，直到在任何一个类中调用finish_pool_queue为止。

线程版本：

'''
Created on Nov 4, 2019

@author: Kevin
'''
from threading import Lock, Thread
from Queue import Queue
import traceback
from helium.loaders.loader_retailers import print_info
from time import sleep
import signal
import os

class ThreadPool(object):
    def __init__(self, queue_threads, *args, **kwargs):
        self.frozen_pool = kwargs.get('frozen_pool', False)
        self.print_queue = kwargs.get('print_queue', True)
        self.pool_results = []
        self.lock = Lock()
        self.queue_threads = queue_threads
        self.queue = Queue()
        self.threads = []

        for i in range(self.queue_threads):
            t = Thread(target=self.make_pool_call)
            t.daemon = True
            t.start()
            self.threads.append(t)

    def make_pool_call(self):
        while True:
            if self.frozen_pool:
                #print '--> Queue is frozen'
                sleep(1)
                continue

            item = self.queue.get()
            if item is None:
                break

            call = item.get('call', None)
            args = item.get('args', [])
            kwargs = item.get('kwargs', {})
            keep_results = item.get('keep_results', False)

            try:
                result = call(*args, **kwargs)

                if keep_results:
                    self.lock.acquire()
                    self.pool_results.append((item, result))
                    self.lock.release()

            except Exception as e:
                self.lock.acquire()
                print e
                traceback.print_exc()
                self.lock.release()
                os.kill(os.getpid(), signal.SIGUSR1)

            self.queue.task_done()

    def finish_pool_queue(self):
        self.frozen_pool = False

        while self.queue.unfinished_tasks > 0:
            if self.print_queue:
                print_info('--> Thread pool... %s' % self.queue.unfinished_tasks)
            sleep(5)

        self.queue.join()

        for i in range(self.queue_threads):
            self.queue.put(None)

        for t in self.threads:
            t.join()

        del self.threads[:]

    def get_pool_results(self):
        return self.pool_results

    def clear_pool_results(self):
        del self.pool_results[:]

进程版本：

    '''
Created on Nov 4, 2019

@author: Kevin
'''
import traceback
from helium.loaders.loader_retailers import print_info
from time import sleep
import signal
import os
from multiprocessing import Queue, Process, Value, Array, JoinableQueue
from dill import dill
import ctypes
from helium.misc.utils import ignore_exception

class ProcessPool(object):
    def __init__(self, queue_processes, *args, **kwargs):
        self.frozen_pool = Value(ctypes.c_bool, kwargs.get('frozen_pool', False))
        self.print_queue = kwargs.get('print_queue', True)
        self.pool_results = Array(ctypes.c_char_p, kwargs.get('pool_result_size', 0))
        self.queue_processes = queue_processes
        self.queue = JoinableQueue()
        self.processes = []

        for i in range(self.queue_processes):
            p = Process(target=self.make_pool_call)
            p.start()
            self.processes.append(p)

        print 'Processes', self.queue_processes

    def make_pool_call(self):
        while True:
            if self.frozen_pool.value:
                #print '--> Queue is frozen'
                sleep(1)
                continue

            item_pickled = self.queue.get()

            if item_pickled is None:
                print '--> Ending'
                self.queue.task_done()
                break

            item = dill.loads(item_pickled)

            call = item.get('call', None)
            args = item.get('args', [])
            kwargs = item.get('kwargs', {})
            keep_results = item.get('keep_results', False)

            try:
                result = call(*args, **kwargs)

                if keep_results:
                    self.pool_results.append((item, result))

            except Exception as e:
                print e
                traceback.print_exc()
                os.kill(os.getpid(), signal.SIGUSR1)

            self.queue.task_done()

    def finish_pool_queue(self):
        self.frozen_pool.value = False

        while self.queue.qsize() > 0:
            if self.print_queue:
                print_info('--> Process pool... %s' % self.queue.qsize())
            sleep(5)

        for i in range(self.queue_processes):
            self.queue.put(None)

        self.queue.join()
        self.queue.close()

        for p in self.processes:
            with ignore_exception: p.join(15)

        with ignore_exception: del self.processes[:]

    def get_pool_results(self):
        return self.pool_results

    def clear_pool_results(self):
        del self.pool_results[:]

def test(eg):
        print 'EG', eg

致电：

tp = ThreadPool(queue_threads=2)
tp.queue.put({'call': test, 'args': [random.randint(0, 100)]})
tp.finish_pool_queue()

或

pp = ProcessPool(queue_processes=2)
pp.queue.put(dill.dumps({'call': test, 'args': [random.randint(0, 100)]}))
pp.queue.put(dill.dumps({'call': test, 'args': [random.randint(0, 100)]}))
pp.finish_pool_queue()

Answer 5

一个多生产者和多消费者的例子，经过验证。应该很容易修改它以涵盖其他情况，单/多生产者，单/多消费者。

from multiprocessing import Process, JoinableQueue
import time
import os

q = JoinableQueue()

def producer():
    for item in range(30):
        time.sleep(2)
        q.put(item)
    pid = os.getpid()
    print(f'producer {pid} done')


def worker():
    while True:
        item = q.get()
        pid = os.getpid()
        print(f'pid {pid} Working on {item}')
        print(f'pid {pid} Finished {item}')
        q.task_done()

for i in range(5):
    p = Process(target=worker, daemon=True).start()

# send thirty task requests to the worker
producers = []
for i in range(2):
    p = Process(target=producer)
    producers.append(p)
    p.start()

# make sure producers done
for p in producers:
    p.join()

# block until all workers are done
q.join()
print('All work completed')

说明：

1. 本示例中有两个生产者和五个消费者。
2. JoinableQueue 用于确保存储在队列中的所有元素都将被处理。 'task_done' 是让工作人员通知元素已完成。 'q.join()' 将等待所有标记为完成的元素。
3. 使用 #2，无需为每个工人加入等待。
4. 但重要的是加入等待每个生产者将元素存储到队列中。否则，程序立即退出。

Answer 6

这是multiprocessing.Queue和multiprocessing.Process的简单用法，它允许调用者向单独的进程发送“事件”和参数，该单独的进程将事件分派给该进程的“ do_”方法。 （Python 3.4及更高版本）

import multiprocessing as mp
import collections

Msg = collections.namedtuple('Msg', ['event', 'args'])

class BaseProcess(mp.Process):
    """A process backed by an internal queue for simple one-way message passing.
    """
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.queue = mp.Queue()

    def send(self, event, *args):
        """Puts the event and args as a `Msg` on the queue
        """
       msg = Msg(event, args)
       self.queue.put(msg)

    def dispatch(self, msg):
        event, args = msg

        handler = getattr(self, "do_%s" % event, None)
        if not handler:
            raise NotImplementedError("Process has no handler for [%s]" % event)

        handler(*args)

    def run(self):
        while True:
            msg = self.queue.get()
            self.dispatch(msg)

用法：

class MyProcess(BaseProcess):
    def do_helloworld(self, arg1, arg2):
        print(arg1, arg2)

if __name__ == "__main__":
    process = MyProcess()
    process.start()
    process.send('helloworld', 'hello', 'world')

send发生在父进程中，do_*发生在子进程中。

我忽略了任何显然会中断运行循环并退出子进程的异常处理。您还可以通过覆盖run来自定义它，以控制阻止或其他操作。

这实际上仅在您具有单个工作进程的情况下有用，但是我认为这是展示具有更多面向对象的常见方案的一个相关答案。

Answer 7

仅举了一个简单而通用的示例，演示了如何在2个独立程序之间通过Queue传递消息。它不会直接回答OP的问题，但应该足够清楚地表明概念。

服务器：

multiprocessing-queue-manager-server.py

import asyncio
import concurrent.futures
import multiprocessing
import multiprocessing.managers
import queue
import sys
import threading
from typing import Any, AnyStr, Dict, Union


class QueueManager(multiprocessing.managers.BaseManager):

    def get_queue(self, ident: Union[AnyStr, int, type(None)] = None) -> multiprocessing.Queue:
        pass


def get_queue(ident: Union[AnyStr, int, type(None)] = None) -> multiprocessing.Queue:
    global q

    if not ident in q:
        q[ident] = multiprocessing.Queue()

    return q[ident]


q: Dict[Union[AnyStr, int, type(None)], multiprocessing.Queue] = dict()
delattr(QueueManager, 'get_queue')


def init_queue_manager_server():
    if not hasattr(QueueManager, 'get_queue'):
        QueueManager.register('get_queue', get_queue)


def serve(no: int, term_ev: threading.Event):
    manager: QueueManager
    with QueueManager(authkey=QueueManager.__name__.encode()) as manager:
        print(f"Server address {no}: {manager.address}")

        while not term_ev.is_set():
            try:
                item: Any = manager.get_queue().get(timeout=0.1)
                print(f"Client {no}: {item} from {manager.address}")
            except queue.Empty:
                continue


async def main(n: int):
    init_queue_manager_server()
    term_ev: threading.Event = threading.Event()
    executor: concurrent.futures.ThreadPoolExecutor = concurrent.futures.ThreadPoolExecutor()

    i: int
    for i in range(n):
        asyncio.ensure_future(asyncio.get_running_loop().run_in_executor(executor, serve, i, term_ev))

    # Gracefully shut down
    try:
        await asyncio.get_running_loop().create_future()
    except asyncio.CancelledError:
        term_ev.set()
        executor.shutdown()
        raise


if __name__ == '__main__':
    asyncio.run(main(int(sys.argv[1])))

客户：

multiprocessing-queue-manager-client.py

import multiprocessing
import multiprocessing.managers
import os
import sys
from typing import AnyStr, Union


class QueueManager(multiprocessing.managers.BaseManager):

    def get_queue(self, ident: Union[AnyStr, int, type(None)] = None) -> multiprocessing.Queue:
        pass


delattr(QueueManager, 'get_queue')


def init_queue_manager_client():
    if not hasattr(QueueManager, 'get_queue'):
        QueueManager.register('get_queue')


def main():
    init_queue_manager_client()

    manager: QueueManager = QueueManager(sys.argv[1], authkey=QueueManager.__name__.encode())
    manager.connect()

    message = f"A message from {os.getpid()}"
    print(f"Message to send: {message}")
    manager.get_queue().put(message)


if __name__ == '__main__':
    main()

用法

服务器：

$ python3 multiprocessing-queue-manager-server.py N

N是一个整数，指示应创建多少个服务器。复制服务器输出的<server-address-N>之一，并将其​​作为每个multiprocessing-queue-manager-client.py的第一个参数。

客户：

python3 multiprocessing-queue-manager-client.py <server-address-1>

结果

服务器：

Client 1: <item> from <server-address-1>

要点：https://gist.github.com/89062d639e40110c61c2f88018a8b0e5

---

UPD ：创建了一个程序包here。

服务器：

import ipcq


with ipcq.QueueManagerServer(address=ipcq.Address.DEFAULT, authkey=ipcq.AuthKey.DEFAULT) as server:
    server.get_queue().get()

客户：

import ipcq


client = ipcq.QueueManagerClient(address=ipcq.Address.DEFAULT, authkey=ipcq.AuthKey.DEFAULT)
client.get_queue().put('a message')