Python：如何让不同的线程读取文件的不同部分

我有一个大约300万行的文件。每一行都包含一些我想解析并将其发布到远程服务调用的数据

如果我按顺序读取该文件，则整个程序需要很长时间才能完成运行

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我在考虑启动一个线程池，每个线程在文件的不同行上迭代（例如：线程1将读取第1行到第10行，线程2将读取第11行到第20行等等），这是典型的映射/减少问题。在python中有没有一种快速的方法来完成这项任务，任何库都可以帮助我完成这项任务。

如果你逐行阅读文件，那么用python实现多线程是不容易的。因为seek（）方法需要知道每行的字节偏移量

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另一种方法是先拆分文件，比如在linux上使用“拆分”。然后启动多个线程分别处理分割文件。

由于GIL，多线程可能对您没有帮助。我想你可以试试多重处理

from multiprocessing import Pool

def f(lines):
    for l in lines:
        print l

if __name__ == '__main__':
    f = open('file')
    total = 2
    lines = f.readlines()
    step = lines/total
    p = Pool(total)
    p.map(f, [lines[0:step],lines[step:step*total-1]])

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您可以根据需要编写自己的MapReduce

请参阅下面的代码（）。此代码读取多个文件并生成输出。您可以修改代码，使每个辅助线程使用不同的文件段

import collections
import itertools
import multiprocessing

class SimpleMapReduce(object):

    def __init__(self, map_func, reduce_func, num_workers=None):
        """
        map_func

          Function to map inputs to intermediate data. Takes as
          argument one input value and returns a tuple with the key
          and a value to be reduced.

        reduce_func

          Function to reduce partitioned version of intermediate data
          to final output. Takes as argument a key as produced by
          map_func and a sequence of the values associated with that
          key.

        num_workers

          The number of workers to create in the pool. Defaults to the
          number of CPUs available on the current host.
        """
        self.map_func = map_func
        self.reduce_func = reduce_func
        self.pool = multiprocessing.Pool(num_workers)

    def partition(self, mapped_values):
        """Organize the mapped values by their key.
        Returns an unsorted sequence of tuples with a key and a sequence of values.
        """
        partitioned_data = collections.defaultdict(list)
        for key, value in mapped_values:
            partitioned_data[key].append(value)
        return partitioned_data.items()

    def __call__(self, inputs, chunksize=1):
        """Process the inputs through the map and reduce functions given.

        inputs
          An iterable containing the input data to be processed.

        chunksize=1
          The portion of the input data to hand to each worker.  This
          can be used to tune performance during the mapping phase.
        """
        map_responses = self.pool.map(self.map_func, inputs, chunksize=chunksize)
        partitioned_data = self.partition(itertools.chain(*map_responses))
        reduced_values = self.pool.map(self.reduce_func, partitioned_data)
        return reduced_values


import multiprocessing
import string

from multiprocessing_mapreduce import SimpleMapReduce

def file_to_words(filename):
    """Read a file and return a sequence of (word, occurances) values.
    """
    STOP_WORDS = set([
            'a', 'an', 'and', 'are', 'as', 'be', 'by', 'for', 'if', 'in', 
            'is', 'it', 'of', 'or', 'py', 'rst', 'that', 'the', 'to', 'with',
            ])
    TR = string.maketrans(string.punctuation, ' ' * len(string.punctuation))

    print multiprocessing.current_process().name, 'reading', filename
    output = []

    with open(filename, 'rt') as f:
        for line in f:
            if line.lstrip().startswith('..'): # Skip rst comment lines
                continue
            line = line.translate(TR) # Strip punctuation
            for word in line.split():
                word = word.lower()
                if word.isalpha() and word not in STOP_WORDS:
                    output.append( (word, 1) )
    return output


def count_words(item):
    """Convert the partitioned data for a word to a
    tuple containing the word and the number of occurances.
    """
    word, occurances = item
    return (word, sum(occurances))


if __name__ == '__main__':
    import operator
    import glob

    input_files = glob.glob('*.rst')

    mapper = SimpleMapReduce(file_to_words, count_words)
    word_counts = mapper(input_files)
    word_counts.sort(key=operator.itemgetter(1))
    word_counts.reverse()

    print '\nTOP 20 WORDS BY FREQUENCY\n'
    top20 = word_counts[:20]
    longest = max(len(word) for word, count in top20)
    for word, count in top20:
        print '%-*s: %5s' % (longest+1, word, count)


$ python multiprocessing_wordcount.py

PoolWorker-1 reading basics.rst
PoolWorker-3 reading index.rst
PoolWorker-4 reading mapreduce.rst
PoolWorker-2 reading communication.rst

TOP 20 WORDS BY FREQUENCY

process         :    80
starting        :    52
multiprocessing :    40
worker          :    37
after           :    33
poolworker      :    32
running         :    31
consumer        :    31
processes       :    30
start           :    28
exiting         :    28
python          :    28
class           :    27
literal         :    26
header          :    26
pymotw          :    26
end             :    26
daemon          :    22
now             :    21
func            :    20

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行的长度是否相同，或者至少是可预测的长度？我是否可以将其视为结果的顺序不必与文件中行的顺序一致？它们的长度不同，但每行都由一个新字符分隔。这是一个有3个字段的tsv文件。@thefourtheye，没错，顺序在这里并不重要。假设所有这些行都需要存储在数据库中。我建议您在尝试优化之前先分析现有代码。取决于它在哪里花费了大部分时间，多线程可能根本没有效果，甚至会使它变慢。