使用islice和multiprocessing批量读取和处理大文本文件

Question

code 没有 return 任何东西，它永远保持运行。请帮助代码片段。仅供参考：我是第一次使用 multiprocessing。

我的本地内存不足，因此从 zip 文件中提取数据。我的想法是使用 islice 一次读取 n 行并使用 process_logBatch().

处理它们

运行此代码在 windows 机器上 - Jupyter Notebook。

import multiprocessing as mp
import zipfile
from itertools import islice
import time
#import pandas as pd  # Unused.

def process_logBatch(next_n_lines):
    l = [random.randint(0,100) for i in range(5)]
    print(l)
    return l

def collect_results(result):
    results.extend(result)

pool = mp.Pool(processes=(mp.cpu_count()-1))

results = []

with zipfile.ZipFile('log.zip', 'r') as z:
    with z.open('log.txt') as f:

        while True:
            print(f.closed)
            next_n_lines = [x.decode("utf-8").strip() for x in islice(f, 2)]

            if not next_n_lines:
                break

            try:
                pool.apply_async(process_logBatch, args=(next_n_lines, ), callback=collect_results)
            except Exception as e:
                print(e)

            if counter == 2:
                break
        pool.close()
        pool.join()

print(results)

Answer 1

有几个问题。一个是 Windows 您需要一个 if __name__ == '__main__': 语句来保护主模块，如多处理模块 documentation.

中标题为 "Safe importing of main module" 的部分所示和讨论的那样

然而，第二件事就没那么容易解决了。每个进程都在自己的内存 space 中运行，因此它们并不都具有相同的 results 列表。为了避免这种情况，我改用 Pool.map_async() 并在所有子流程结束时收集结果。

这是我认为可行的方法（基于您的示例代码）：

import multiprocessing as mp
import zipfile
from itertools import islice
import time
#import pandas as pd  # Unused.
import random  # Added.

def process_logBatch(next_n_lines):
    l = [random.randint(0,100) for i in range(5)]
    print(l)
    return l

if __name__ == '__main__':

# Not longer needed.
#    def collect_results(result):
#        results.extend(result)

    pool = mp.Pool(processes=(mp.cpu_count()-1))

    with zipfile.ZipFile('log.zip', 'r') as z:
        with z.open('log.txt') as f:

            counter = 0  # Added to avoid NameError because undefined.

            while True:
                next_n_lines = [x.decode("utf-8").strip() for x in islice(f, 2)]

                if not next_n_lines:
                    break

                try:
                    results = pool.map_async(process_logBatch, next_n_lines)
                except Exception as e:
                    print(e)

                if counter == 2:
                    break

            pool.close()
            pool.join()

    print(results.get())

使用islice和multiprocessing批量读取和处理大文本文件

Reading and processing large text file in batches using islice and multiprocessing

python

windows

iterator

large-files

python-multiprocessing