Python 使用管理器、池和共享列表的多处理并发不起作用

Question

我正在学习 python 多处理，我正在尝试使用此功能来填充一个列表，其中包含操作系统中存在的所有文件。 但是，我编写的代码仅按顺序执行。

#!/usr/bin/python
import os
import multiprocessing
tld = [os.path.join("/", f) for f in os.walk("/").next()[1]] #Gets a top level directory names inside "/"
manager = multiprocessing.Manager()
files = manager.list()


def get_files(x):
    for root, dir, file in os.walk(x):
        for name in file:
            files.append(os.path.join(root, name))

mp = [multiprocessing.Process(target=get_files, args=(tld[x],))
      for x in range(len(tld))]

for i in mp:
    i.start()
    i.join()
print len(files)

当我检查进程树时，我只能看到生成了一个智利进程。 （man pstree 说 {} 表示父进程产生的子进程。）

---bash(10949)---python(12729)-+-python(12730)---{python}(12752)
                               `-python(12750)`

我正在寻找的是，为每个 tld 目录生成一个进程，填充共享列表files ，这将是大约 10-15 个进程，具体取决于目录的数量。 我究竟做错了什么？

编辑：：

我使用multiprocessing.Pool创建工作线程，这次生成了进程，但是当我尝试使用multiprocessing.Pool.map()时出现错误。 我指的是 python 文档中显示的以下代码

from multiprocessing import Pool
def f(x):
return x*x

if __name__ == '__main__':
    p = Pool(5)
    print(p.map(f, [1, 2, 3])) 

按照该示例，我将代码重写为

import os
import multiprocessing
tld = [os.path.join("/", f) for f in os.walk("/").next()[1]]
manager = multiprocessing.Manager()
pool = multiprocessing.Pool(processes=len(tld))
print pool
files = manager.list()
def get_files(x):
    for root, dir, file in os.walk(x):
        for name in file:
            files.append(os.path.join(root, name))
pool.map(get_files, [x for x in tld])
pool.close()
pool.join()
print len(files)

它正在分叉多个进程。

---bash(10949)---python(12890)-+-python(12967)
                               |-python(12968)
                               |-python(12970)
                               |-python(12971)
                               |-python(12972)
                               ---snip---

但是代码错误地说

Process PoolWorker-2: Traceback (most recent call last): File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap Traceback (most recent call last): File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap self.run() File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run self._target(*self._args, **self._kwargs) File "/usr/lib/python2.7/multiprocessing/pool.py", line 102, in worker File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run task = get() File "/usr/lib/python2.7/multiprocessing/queues.py", line 376, in get return recv() AttributeError: 'module' object has no attribute 'get_files' self._target(*self._args, **self._kwargs) self.run() task = get() File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run self.run() File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run self._target(*self._args, **self._kwargs) File "/usr/lib/python2.7/multiprocessing/pool.py", line 102, in worker File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run task = get() File "/usr/lib/python2.7/multiprocessing/queues.py", line 376, in get AttributeError: 'module' object has no attribute 'get_files' self.run()

我在这里做错了什么，为什么会出现 get_files() function 错误？

Answer 1

这仅仅是因为您在定义函数get_files之前实例化了池：

import os
import multiprocessing

tld = [os.path.join("/", f) for f in os.walk("/").next()[1]]
manager = multiprocessing.Manager()

files = manager.list()
def get_files(x):
    for root, dir, file in os.walk(x):
        for name in file:
            files.append(os.path.join(root, name))

pool = multiprocessing.Pool(processes=len(tld)) # Instantiate the pool here

pool.map(get_files, [x for x in tld])
pool.close()
pool.join()
print len(files)

流程的总体思想是，在启动流程的那一刻，您便会分叉主流程的内存。 因此，在派生之后在主流程中完成的任何定义都不会在子流程中。

如果您想要共享内存，则可以使用threading库，但是会遇到一些问题（参见： 全局解释器锁 ）

Answer 2

我遇到了这个并在 Python 3.x 上尝试了接受的答案，但由于几个原因它不起作用。 这是一个确实有效的修改版本（截至撰写本文时为 Python 3.10.1）：

import multiprocessing
import os


def get_files(x, files_):
    proc = multiprocessing.Process()
    for root, dir, file in os.walk(x):
        for name in file:
            full_path = os.path.join(root, name)
            # print(filename"worker:{proc.name} path:{full_path}")
            files_.append(full_path)


if __name__ == '__main__':
    # See https://docs.python.org/3/library/multiprocessing.html
    with multiprocessing.Manager() as manager:
        # The code will count the number of result_files under the specified root:
        root = '/'

        # Create the top level list of folders which will be walked (and result_files counted)
        tld = [os.path.join(os.pathsep, root, filename) for filename in next(os.walk(root))[1]]

        # Creates result list object in the manager, which is passed to the workers to collect results into.
        result_files = manager.list()

        # Create a pool of workers, with the size being equal to the number of top level folders:
        pool = multiprocessing.Pool(processes=len(tld))

        # Use starmap() instead of map() to allow passing multiple arguments (e.g. the folder and the result_files list).
        pool.starmap(get_files, [(folder, result_files) for folder in tld])

        pool.close()
        pool.join()

        # The result, the count of the number of result_files.
        print(len(result_files))