从大文件中分块数据以进行多处理? [英] Chunking data from a large file for multiprocessing?

问题描述

我正在尝试使用多处理并行化应用程序一个非常大的 csv 文件(64MB 到 500MB)，逐行做一些工作，然后输出一个小的、固定大小的文件文件.

I'm trying to a parallelize an application using multiprocessing which takes in a very large csv file (64MB to 500MB), does some work line by line, and then outputs a small, fixed size file.

目前我做了一个list(file_obj)，不幸的是它被完全加载了进入内存(我认为)然后我将该列表分成 n 个部分，n 是我想运行的进程数.然后我在分解的地方做一个 pool.map()列表.

Currently I do a list(file_obj), which unfortunately is loaded entirely into memory (I think) and I then I break that list up into n parts, n being the number of processes I want to run. I then do a pool.map() on the broken up lists.

与单一的相比，这似乎有一个非常非常糟糕的运行时间线程化，只需打开文件并迭代它的方法.有人可以吗提出更好的解决方案?

This seems to have a really, really bad runtime in comparison to a single threaded, just-open-the-file-and-iterate-over-it methodology. Can someone suggest a better solution?

此外，我需要在保留的组中处理文件的行某个列的值.这些行组本身可以拆分，但对于这一列，任何组都不应包含多个值.

Additionally, I need to process the rows of the file in groups which preserve the value of a certain column. These groups of rows can themselves be split up, but no group should contain more than one value for this column.

推荐答案

list(file_obj) 当 fileobj 很大时，可能需要大量内存.我们可以通过使用 itertools 来提取行块来减少内存需求因为我们需要他们.

list(file_obj) can require a lot of memory when fileobj is large. We can reduce that memory requirement by using itertools to pull out chunks of lines as we need them.

特别是，我们可以使用

reader = csv.reader(f)
chunks = itertools.groupby(reader, keyfunc)

将文件拆分为可处理的块，以及

to split the file into processable chunks, and

groups = [list(chunk) for key, chunk in itertools.islice(chunks, num_chunks)]
result = pool.map(worker, groups)

让多处理池一次处理 num_chunks 个块.

to have the multiprocessing pool work on num_chunks chunks at a time.

通过这样做，我们大约只需要足够的内存来在内存中保存几个 (num_chunks) 块，而不是整个文件.

By doing so, we need roughly only enough memory to hold a few (num_chunks) chunks in memory, instead of the whole file.

import multiprocessing as mp
import itertools
import time
import csv

def worker(chunk):
    # `chunk` will be a list of CSV rows all with the same name column
    # replace this with your real computation
    # print(chunk)
    return len(chunk)  

def keyfunc(row):
    # `row` is one row of the CSV file.
    # replace this with the name column.
    return row[0]

def main():
    pool = mp.Pool()
    largefile = 'test.dat'
    num_chunks = 10
    results = []
    with open(largefile) as f:
        reader = csv.reader(f)
        chunks = itertools.groupby(reader, keyfunc)
        while True:
            # make a list of num_chunks chunks
            groups = [list(chunk) for key, chunk in
                      itertools.islice(chunks, num_chunks)]
            if groups:
                result = pool.map(worker, groups)
                results.extend(result)
            else:
                break
    pool.close()
    pool.join()
    print(results)

if __name__ == '__main__':
    main()

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