Python多处理：使用队列进行多处理并不比单个进程代码

Question

我做了一些多处理示例，如下所示：

import time
import multiprocessing as mp
import requests

from bs4 import BeautifulSoup as bs
from multiprocessing import Pool

manager = mp.Manager()
messages_dict = manager.dict()
messages_dict['value'] = ''

def complex_task():
    req = requests.get('https://stackoverflow.com')
    html = req.text
    soup = bs(html, 'html.parser')
    my_titles = soup.select('h3 > a')
    data = []

    for title in my_titles:
        data.append(title.get('href'))

    time.sleep(2)

class abc:
    def all(self):
        complex_task()
        return "This is abc \n what a abc!\n"

class bcd:
    def all(self):
        complex_task()
        return "This is bcd \n what a bcd!\n"

class cde:
    def all(self):
        complex_task()
        return "This is cde \n what a cde!\n"

class ijk:
    def all(self):
        complex_task()
        return "This is ijk \n what a ijk!\n"


def crawler(sites, ps_queue):
    for site in sites:
        ps_queue.put(site)

def message_collector(ps_queue):
    while True:
        site = ps_queue.get()
        messages_dict['value'] += site.all()
        ps_queue.task_done()


def main():
    site_list = [abc(), bcd(), cde(), ijk()]

    #
    # Single process
    #
    # start_time = time.time()
    # messages = ''
    # for site in site_list:
    #     messages += site.all()
    # print(messages)


    #
    # Multi process
    #
    ps_queue = mp.JoinableQueue()
    message_collector_proc = mp.Process(
        target=message_collector,
        args=(ps_queue, )
    )
    message_collector_proc.daemon = True

    start_time = time.time()

    message_collector_proc.start()
    crawler(site_list, ps_queue)
    ps_queue.join()

    print(messages_dict['value'])


    print(time.time() - start_time)

if __name__ == "__main__":
    main()

但事实证明，单个进程和多进程程序显示相同的运行时速度。正如您在上面的代码中看到的那样，main()中有两部分代码：Multi process和Single process。

2013年底的Macbook Pro运行时（OS X Sierra）： - 单一过程：16.92385721206665 - 多进程：16.362822771072388

差不多......

我不知道为什么会这样......需要你的帮助！