在Pool.map中使用生成器作为可迭代对象是否合理？

Absolutely! Pool.map() does support generators as its second iterable parameter—you don’t have to convert your generator to a list, numpy array, or other sequence type first.

Why this works #

The Pool.map() method only requires its second argument to be an iterable, and generators are fully valid iterables. When you pass a generator, Pool.map() will iterate through it to split the elements into chunks, then distribute those chunks to worker processes in the pool.

Example code to test this #

Here’s a quick working example to confirm:

from multiprocessing import Pool

def process_num(x):
    return x * 2

if __name__ == "__main__":
    # Use a generator expression as the iterable
    with Pool(3) as pool:
        results = pool.map(process_num, (num for num in range(5)))
        print(results)  # Output: [0, 2, 4, 6, 8]

Important caveats to keep in mind #

• Infinite generators will break things: Since Pool.map() iterates through the entire iterable upfront to split into chunks, passing an infinite generator will cause your program to hang indefinitely or consume all available memory as it tries to generate endless elements.
• Memory usage for large generators: Even though you’re using a generator (which is memory-efficient on its own), Pool.map() still needs to collect all elements from the generator first before distributing work. If your generator produces millions of elements, this will use roughly the same amount of memory as converting it to a list. For large datasets, consider using Pool.imap() or Pool.imap_unordered() instead—these methods return results incrementally without loading all elements into memory at once.

内容的提问来源于stack exchange，提问作者steffen