问题描述

当我使用生成器作为带有 multiprocessing.Pool.map 函数的可迭代参数时:

When I use a generator as an iterable argument with multiprocessing.Pool.map function:

pool.map(func, iterable=(x for x in range(10)))

在 func 被调用之前，生成器似乎已经完全耗尽.

It seems that the generator is fully exhausted before func is ever called.

我想生成每个项目并将其传递给每个进程，谢谢

I want to yield each item and pass it to each process, thanks

推荐答案

multiprocessing.map 在处理之前将没有 __len__ 方法的可迭代对象转换为列表.这样做是为了帮助计算块大小，池使用它来对工作参数进行分组并降低调度作业的往返成本.这不是最优的，尤其是当 chunksize 为 1 时，但由于 map 必须以一种或另一种方式耗尽迭代器，它通常不是一个重大问题.

multiprocessing.map converts iterables without a __len__ method to a list before processing. This is done to aid the calculation of chunksize, which the pool uses to group worker arguments and reduce the round trip cost of scheduling jobs. This is not optimal, especially when chunksize is 1, but since map must exhaust the iterator one way or the other, its usually not a significant issue.

相关代码在pool.py中.注意它对 len 的使用:

The relevant code is in pool.py. Notice its use of len:

def _map_async(self, func, iterable, mapper, chunksize=None, callback=None,
        error_callback=None):
    '''
    Helper function to implement map, starmap and their async counterparts.
    '''
    if self._state != RUN:
        raise ValueError("Pool not running")
    if not hasattr(iterable, '__len__'):
        iterable = list(iterable)

    if chunksize is None:
        chunksize, extra = divmod(len(iterable), len(self._pool) * 4)
        if extra:
            chunksize += 1
    if len(iterable) == 0:
        chunksize = 0

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