无法将函数并行映射到 tarfile 成员

我有一个包含 bz2 压缩文件的 tar 文件。我想应用该功能clean_file到每个 bz2 文件，并整理结果。在系列中，使用循环很容易：

import pandas as pd
import json
import os
import bz2
import itertools
import datetime
import tarfile
from multiprocessing import Pool

def clean_file(member):
    if '.bz2' in str(member):

        f = tr.extractfile(member)

        with bz2.open(f, "rt") as bzinput:
            dicts = []
            for i, line in enumerate(bzinput):
                line = line.replace('"name"}', '"name":" "}')
                dat = json.loads(line)
                dicts.append(dat)

        bzinput.close()
        f.close()
        del f, bzinput

        processed = dicts[0]
        return processed

    else:
        pass


# Open tar file and get contents (members)
tr = tarfile.open('data.tar')
members = tr.getmembers()
num_files = len(members)


# Apply the clean_file function in series
i=0
processed_files = []
for m in members:
    processed_files.append(clean_file(m))
    i+=1
    print('done '+str(i)+'/'+str(num_files))
    

但是，我需要能够并行执行此操作。我正在尝试使用的方法Pool像这样：

# Apply the clean_file function in parallel
if __name__ == '__main__':
   with Pool(2) as p:
      processed_files = list(p.map(clean_file, members))

但这会返回一个 OSError：

Traceback (most recent call last):
  File "/Users/johnfoley/opt/anaconda3/envs/racing_env/lib/python3.6/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "parse_data.py", line 19, in clean_file
    for i, line in enumerate(bzinput):
  File "/Users/johnfoley/opt/anaconda3/envs/racing_env/lib/python3.6/bz2.py", line 195, in read1
    return self._buffer.read1(size)
  File "/Users/johnfoley/opt/anaconda3/envs/racing_env/lib/python3.6/_compression.py", line 68, in readinto
    data = self.read(len(byte_view))
  File "/Users/johnfoley/opt/anaconda3/envs/racing_env/lib/python3.6/_compression.py", line 103, in read
    data = self._decompressor.decompress(rawblock, size)
OSError: Invalid data stream
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "parse_data.py", line 53, in <module>
    processed_files = list(tqdm.tqdm(p.imap(clean_file, members), total=num_files))
  File "/Users/johnfoley/opt/anaconda3/envs/racing_env/lib/python3.6/site-packages/tqdm/std.py", line 1167, in __iter__
    for obj in iterable:
  File "/Users/johnfoley/opt/anaconda3/envs/racing_env/lib/python3.6/multiprocessing/pool.py", line 735, in next
    raise value
OSError: Invalid data stream

所以我猜这种方式无法正确访问 data.tar 或其他内容中的文件。如何并行应用该功能？

我猜这适用于任何包含 bz2 文件的 tar 存档，但这是我重现错误的数据：https://github.com/johnf1004/reproduct_tar_error https://github.com/johnf1004/reproduce_tar_error

您没有指定您正在运行的平台，但我怀疑它是 Windows，因为您有...

if __name__ == '__main__':
    main()

...这对于在使用操作系统功能的平台上创建进程的代码是必需的spawn用于创建新流程。但这也意味着当创建一个新进程（例如您正在创建的进程池中的所有进程）时，每个进程都会从程序的最顶部重新执行源程序。这意味着每个池进程正在执行以下代码：

tr = tarfile.open('data.tar')
members = tr.getmembers()
num_files = len(members)

但是，我不明白为什么这本身会导致错误，但我不能确定。然而，问题可能是，这是在调用工作函数之后执行的，clean_file正在被调用，所以tr尚未设置。如果这段代码前面clean_file它可能有效，但这只是一个猜测。当然提取成员members = tr.getmembers()在每个池进程中都是浪费的。每个进程都需要打开 tar 文件，最好只打开一次。

但很明显，您发布的堆栈跟踪与您的代码不匹配。你展示：

Traceback (most recent call last):
  File "parse_data.py", line 53, in <module>
    processed_files = list(tqdm.tqdm(p.imap(clean_file, members), total=num_files))

然而你的代码没有任何参考tqdm或使用方法imap。现在，当您发布的代码与产生异常的代码不太匹配时，分析您的实际问题变得更加困难。

如果您在 Mac 上运行，它可能正在使用fork要创建新进程，当主进程创建了多个线程（您不一定会看到，也许是通过tarfile模块），然后创建一个新进程，我已指定代码以确保spawn用于创建新流程。无论如何，下面的代码should工作。它还引入了一些优化。如果没有，请发布新的堆栈跟踪。

import pandas as pd
import json
import os
import bz2
import itertools
import datetime
import tarfile
from multiprocessing import get_context

def open_tar():
    # open once for each process in the pool
    global tr
    tr = tarfile.open('data.tar')

def clean_file(member):
    f = tr.extractfile(member)

    with bz2.open(f, "rt") as bzinput:
        for line in bzinput:
            line = line.replace('"name"}', '"name":" "}')
            dat = json.loads(line)
            # since you are returning just the first occurrence:
            return dat

def main():
    with tarfile.open('data.tar') as tr:
        members = tr.getmembers()
    # just pick members where '.bz2' is in member:
    filtered_members = filter(lambda member: '.bz2' in str(member), members)
    ctx = get_context('spawn')
    # open tar file just once for each process in the pool:
    with ctx.Pool(initializer=open_tar) as pool:
        processed_files = pool.map(clean_file, filtered_members)
        print(processed_files)

# required for when processes are created using spawn:
if __name__ == '__main__':
    main()