如何在Python中使用多处理队列？

我很难理解多处理队列如何在python上工作以及如何实现它。假设我有两个python模块，它们从共享文件访问数据，让我们将这两个模块称为writer和Reader。我的计划是使读取器和写入器都将请求放入两个单独的多处理队列中，然后让第三个进程将这些请求循环弹出并照此执行。

我的主要问题是我真的不知道如何正确实现multiprocessing.queue，您不能为每个进程真正实例化对象，因为它们将是单独的队列，如何确保所有进程都与共享队列相关（或在这种情况下，排队）

我的主要问题是我真的不知道如何正确实现multiprocessing.queue，您不能为每个进程真正实例化对象，因为它们将是单独的队列，如何确保所有进程都与共享队列相关（或在这种情况下，排队）

这是读取器和写入器共享一个队列的简单示例。写入器向读取器发送一堆整数。当写入器的数字用完时，它将发送“ DONE”（完成），让读取器知道退出读取循环。

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)))

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

下面是一个死的简单使用multiprocessing.Queue，并multiprocessing.Process允许呼叫者发送一个“事件”加上参数单独的进程调度该事件对进程“的do_”的方法。（Python 3.4以上）

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')

将send在父进程发生，do_*发生在子进程。

我忽略了任何明显会中断运行循环并退出子进程的异常处理。您也可以通过覆盖run来自定义它以控制阻止或其他任何操作。

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一个面向对象多一点的常见方案的一个相关答案。

在尝试建立一种使用队列进行大熊猫数据帧传递的多处理方式时，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。

任何编码示例之前的一些思考。由于queue.Empty或queue.qsize()或任何其他类似方法对流量控制不可靠，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少了一些，那么祝您调试顺利。

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的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

一名工人将获得岗哨价值，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，则应在确定完成工作之前以确定性的方式独立地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，该par_proc()函数将收到一个任务列表，其中包括应与这些函数一起执行的任务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

这是一个针对上面的代码运行测试

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

希望对您有所帮助。

我们实现了这两个版本，一个是可以执行多种类型的可调用对象的简单多线程池，这使我们的生活变得更加轻松；而第二个版本使用了process，后者在可调用性和需求以及对莳萝的额外调用方面不太灵活。

将Frozen_pool设置为true将冻结执行，直到在任一类中调用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[:]

处理版本：

  '''
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, Lock,\
    RawArray, Manager
from dill import dill
import ctypes
from helium.misc.utils import ignore_exception
from mem_top import mem_top
import gc

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.manager = Manager()
        self.pool_results = self.manager.list()
        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:
                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(dill.dumps((item, result)))
                else:
                    del call, args, kwargs, keep_results, 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, callable=None):
        self.frozen_pool.value = False

        while self.queue._unfinished_tasks.get_value() > 0:
            if self.print_queue:
                print_info('--> Process pool... %s' % (self.queue._unfinished_tasks.get_value()))

            if callable:
                callable()

            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(10)
            with ignore_exception: p.terminate()

        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()

要么

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()

刚刚做了一个简单而通用的示例，用于演示在2个独立程序之间通过Queue传递消息。它不能直接回答OP的问题，但应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。<server-address-N>服务器将其中一个输出复制并使其成为每个输出的第一个参数multiprocessing-queue-manager-client.py。

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：在此处创建了一个程序包。

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')