如何在 Python 中使用多处理队列？

问题描述：

我在尝试理解多处理队列在 Python 上的工作原理以及如何实现它时遇到了很多麻烦。假设我有两个 Python 模块可以访问共享文件中的数据，我们将这两个模块称为写入器和读取器。我的计划是让读取器和写入器将请求放入两个单独的多处理队列中，然后让第三个进程在循环中弹出这些请求并按此执行。

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，因为它们将是单独的队列，你如何确保所有进程都与共享队列相关（或者在这种情况下，队列）

解决方案 1：

简短摘要

截至 CY2023，此答案中描述的技术已相当过时。如今，请使用下面的concurrent.futures.ProcessPoolExecutor()代替multiprocessing...

这个答案描述了使用的优点和缺点concurrent.futures.ProcessPoolExecutor()。仅供参考，有时使用多个python进程而不是线程来从并发中获得最大收益。也就是说，只要有足够的CPU活动来避免GIL（例如发送/接收网络流量的活动），python线程就可以很好地工作。

原始答案

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，因为它们将是单独的队列，你如何确保所有进程都与共享队列相关（或者在这种情况下，队列）

这是一个读者和写者共享一个队列的简单例子...写者发送一堆整数给读者；当写者用完数字时，它会发送“DONE”，让读者知道要跳出读取循环。

您可以根据需要生成任意数量的读取器进程......

from multiprocessing import Process, Queue
import time
import sys


def reader_proc(queue):
    """Read from the queue; this spawns as a separate Process"""
    while True:
        msg = queue.get()  # Read from the queue and do nothing
        if msg == "DONE":
            break


def writer(count, num_of_reader_procs, queue):
    """Write integers into the queue.  A reader_proc() will read them from the queue"""
    for ii in range(0, count):
        queue.put(ii)  # Put 'count' numbers into queue

    ### Tell all readers to stop...
    for ii in range(0, num_of_reader_procs):
        queue.put("DONE")


def start_reader_procs(qq, num_of_reader_procs):
    """Start the reader processes and return all in a list to the caller"""
    all_reader_procs = list()
    for ii in range(0, num_of_reader_procs):
        ### reader_p() reads from qq as a separate process...
        ###    you can spawn as many reader_p() as you like
        ###    however, there is usually a point of diminishing returns
        reader_p = Process(target=reader_proc, args=((qq),))
        reader_p.daemon = True
        reader_p.start()  # Launch reader_p() as another proc

        all_reader_procs.append(reader_p)

    return all_reader_procs


if __name__ == "__main__":
    num_of_reader_procs = 2
    qq = Queue()  # writer() writes to qq from _this_ process
    for count in [10**4, 10**5, 10**6]:
        assert 0 < num_of_reader_procs < 4
        all_reader_procs = start_reader_procs(qq, num_of_reader_procs)

        writer(count, len(all_reader_procs), qq)  # Queue stuff to all reader_p()
        print("All reader processes are pulling numbers from the queue...")

        _start = time.time()
        for idx, a_reader_proc in enumerate(all_reader_procs):
            print("    Waiting for reader_p.join() index %s" % idx)
            a_reader_proc.join()  # Wait for a_reader_proc() to finish

            print("        reader_p() idx:%s is done" % idx)

        print(
            "Sending {0} integers through Queue() took {1} seconds".format(
                count, (time.time() - _start)
            )
        )
        print("")

解决方案 2：

这是一个非常简单的用法multiprocessing.Queue，multiprocessing.Process它允许调用者将“事件”和参数发送到单独的进程，该进程将事件分派到进程上的“do_”方法。（Python 3.4+）

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')

这send发生在父进程中，这do_*发生在子进程中。

我省略了任何明显会中断运行循环并退出子进程的异常处理。您还可以通过重写来run控制阻塞或其他任何内容来自定义它。

这实际上仅在您只有一个工作进程的情况下才有用，但我认为这是该问题的相关答案，用于展示具有更多面向对象的常见场景。

解决方案 3：

在尝试建立一种使用队列传递大型 pandas 数据帧的多处理方法时，我查看了 stack overflow 和网络上的多个答案。在我看来，每个答案都在重复同一种解决方案，而没有考虑到在设置此类计算时肯定会遇到的大量边缘情况。问题是有很多事情同时在起作用。任务数量、工作者数量、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，大多数答案都没有说明如何进行同步。所以，这是我摆弄了几个小时后得出的结论，希望它足够通用，能让大多数人觉得它有用。

在编写任何代码示例之前，先思考一下。由于queue.Empty或queue.qsize()或任何其他类似方法对于流控制不可靠，因此任何类似的代码

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

是假的。即使几毫秒后队列中又出现了另一个任务，这也会杀死该工作线程。工作线程将无法恢复，一段时间后，所有工作线程都会消失，因为它们会随机发现队列暂时为空。最终结果是主多处理函数（进程上有 join() 的函数）将在所有任务均未完成的情况下返回。很好。如果您有数千个任务，但有几个任务缺失，那么祝您调试顺利。

另一个问题是使用标记值。许多人建议在队列中添加标记值来标记队列的末尾。但究竟要标记给谁呢？如果有 N 个工人，假设 N 是可用核心的数量，那么单个标记值只会将队列的末尾标记给一个工人。当没有剩余工作时，所有其他工人都会等待更多工作。我见过的典型例子是

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

一名工人将获得标记值，而其余工人将无限期等待。我遇到的帖子都没有提到您需要将标记值提交到队列的次数至少与工人的次数相同，以便所有工人都能获得它。

另一个问题是任务执行期间异常的处理。同样，这些异常应该被捕获和管理。此外，如果您有一个completed_tasks队列，您应该在决定任务完成之前，以确定性的方式独立计算队列中有多少项。同样，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()函数将接收一个任务列表，其中包括应执行这些任务的函数以及任何命名参数和值。

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

下面是对上述代码的测试

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

还有一个例外

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

希望这对您有帮助。

解决方案 4：

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

解决方案 5：

只是举了一个简单而通用的例子来演示如何在两个独立程序之间通过队列传递消息。它并没有直接回答原帖者的问题，但应该足够清楚地表明这个概念。

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，表示应创建多少个服务器。复制<server-address-N>服务器的一个输出并将其作为每个的第一个参数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 ：在此创建了一个包。

服务器：

import ipcq


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

客户：

import ipcq


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

解决方案 6：

我们实现了两个版本，一个简单的多线程池，可以执行多种类型的可调用函数，使我们的生活变得更加轻松；第二个版本使用进程，但在可调用函数方面灵活性较差，需要额外调用 dill。

将 frosty_pool 设置为 true 将冻结执行，直到任一类中调用 finish_pool_queue 为止。

主题版本：

'''
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[:]

流程版本：

  '''
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, Lock,\n    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

使用以下方式拨打电话：

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

或者

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()

解决方案 7：

已验证的多生产者和多消费者示例。应该很容易对其进行修改以涵盖其他情况，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')

解释：

1. 此示例中有两个生产者和五个消费者。

2. JoinableQueue 用于确保队列中存储的所有元素都将被处理。'task_done' 用于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，就不需要为每个工人加入等待。

4. 但必须等待每个生产者将元素存入队列。否则程序将立即退出。