如何在连续循环中使用PYTHON多处理池

问题描述

我正在使用python多处理库来执行Selify脚本。我的代码如下：

#-- start and join multiple threads ---
thread_list = []
total_threads=10 #-- no of parallel threads
for i in range(total_threads):
    t = Process(target=get_browser_and_start, args=[url,nlp,pixel])
    thread_list.append(t)
    print "starting thread..."
    t.start()

for t in thread_list:
    print "joining existing thread..."
    t.join()

根据我对join()函数的理解，它将等待每个进程完成。但我希望流程一发布，就会被分配另一项任务来执行新功能。

可以这样理解：

假设第一个实例中启动了8个进程。

no_of_tasks_to_perform = 100

for i in range(no_of_tasks_to_perform):
    processes start(8)
    if process no 2 finished executing, start new process
    maintain 8 process at any point of time till 
    "i" is <= no_of_tasks_to_perform

---

解决方案

与其时不时地启动新的进程，不如尝试将所有任务放到一个multiprocessing.Queue()中，并启动8个长时间运行的进程，在每个进程中不断访问任务队列以获取新任务，然后执行作业，直到不再有任务。

在您的情况下，更像是这样：

from multiprocessing import Queue, Process

def worker(queue):
    while not queue.empty():
        task = queue.get()

        # now start to work on your task
        get_browser_and_start(url,nlp,pixel) # url, nlp, pixel can be unpacked from task

def main():
    queue = Queue()

    # Now put tasks into queue
    no_of_tasks_to_perform = 100

    for i in range(no_of_tasks_to_perform):
        queue.put([url, nlp, pixel, ...]) 

    # Now start all processes
    process = Process(target=worker, args=(queue, ))
    process.start()
    ...
    process.join()