使用多处理写入文件

Question

我在python中遇到以下问题.

我需要并行进行一些计算,我需要在文件中顺序编写结果.所以我创建了一个接收multiprocessing.Queue文件句柄的函数,进行计算并在文件中打印结果:

import multiprocessing
from multiprocessing import Process, Queue
from mySimulation import doCalculation   

# doCalculation(pars) is a function I must run for many different sets of parameters and collect the results in a file

def work(queue, fh):
while True:
    try:
        parameter = queue.get(block = False)
        result = doCalculation(parameter) 
        print >>fh, string
    except:
        break


if __name__ == "__main__":
    nthreads = multiprocessing.cpu_count()
    fh = open("foo", "w")
    workQueue = Queue()
    parList = # list of conditions for which I want to run doCalculation()
    for x in parList:
        workQueue.put(x)
    processes = [Process(target = writefh, args = (workQueue, fh)) for i in range(nthreads)]
    for p in processes:
       p.start()
    for p in processes:
       p.join()
    fh.close()

但是脚本运行后文件结束为空.我试图将worker()函数更改为:

def work(queue, filename):
while True:
    try:
        fh = open(filename, "a")
        parameter = queue.get(block = False)
        result = doCalculation(parameter) 
        print >>fh, string
        fh.close()
    except:
        break

并将文件名作为参数传递.然后它按我的意图工作.当我尝试按顺序执行相同的操作时,如果没有多处理,它也可以正常工作.

为什么它在第一个版本中没有用？我看不出问题.

另外:我可以保证两个进程不会同时尝试写入文件吗？

---

编辑:

谢谢.我现在明白了.这是工作版本:

import multiprocessing
from multiprocessing import Process, Queue
from time import sleep
from random import uniform

def doCalculation(par):
    t = uniform(0,2)
    sleep(t)
    return par * par  # just to simulate some calculation

def feed(queue, parlist):
    for par in parlist:
            queue.put(par)

def calc(queueIn, queueOut):
    while True:
        try:
            par = queueIn.get(block = False)
            print "dealing with ", par, "" 
            res = doCalculation(par)
            queueOut.put((par,res))
        except:
            break

def write(queue, fname):
    fhandle = open(fname, "w")
    while True:
        try:
            par, res = queue.get(block = False)
            print >>fhandle, par, res
        except:
            break
    fhandle.close()

if __name__ == "__main__":
    nthreads = multiprocessing.cpu_count()
    fname = "foo"
    workerQueue = Queue()
    writerQueue = Queue()
    parlist = [1,2,3,4,5,6,7,8,9,10]
    feedProc = Process(target = feed , args = (workerQueue, parlist))
    calcProc = [Process(target = calc , args = (workerQueue, writerQueue)) for i in range(nthreads)]
    writProc = Process(target = write, args = (writerQueue, fname))


    feedProc.start()
    for p in calcProc:
        p.start()
    writProc.start()

    feedProc.join ()
    for p in calcProc:
        p.join()
    writProc.join ()

Answer 1

你真的应该使用两个队列和三种不同的处理方式.

1. 把东西放入队列#1.

2. 从队列#1中获取东西并进行计算,将东西放入队列#2.您可以拥有其中的许多,因为它们从一个队列中获得并安全地放入另一个队列.

3. 从队列#2中获取内容并将其写入文件.你必须拥有其中的一个,而不是更多.它"拥有"该文件,保证原子访问,并绝对保证文件干净,一致地编写.

Answer 2

如果有人正在寻找一种简单的方法来做到这一点，这可以为您提供帮助。我认为以这种方式进行操作没有任何不利之处。如果有，请告诉我。

import multiprocessing 
import re

def mp_worker(item):
    # Do something
    return item, count

def mp_handler():
    cpus = multiprocessing.cpu_count()
    p = multiprocessing.Pool(cpus)
    # The below 2 lines populate the list. This listX will later be accessed parallely. This can be replaced as long as listX is passed on to the next step.
    with open('ExampleFile.txt') as f:
        listX = [line for line in (l.strip() for l in f) if line]
    with open('results.txt', 'w') as f:
        for result in p.imap(mp_worker, listX):
            # (item, count) tuples from worker
            f.write('%s: %d\n' % result)

if __name__=='__main__':
    mp_handler()

来源：Python：使用多处理池时，写入带有队列的单个文件