您的迭代器it 必须生成单个值（每个值都可以是“复杂的”，例如元组或列表）。现在我们有：
>>> it
<itertools.imap object at 0x000000000283DB70>
>>> list(it)
[<itertools.ifilter object at 0x000000000283DC50>, <itertools.ifilter object at 0x000000000283DF98>, <itertools.ifilter object at 0x000000000283DBE0>, <itertools.ifilter object at 0x000000000283DF60>, <itertools.ifilter object at 0x000000000283DB00>, <itertools.ifilter object at 0x000000000283DCC0>, <itertools.ifilter object at 0x000000000283DD30>, <itertools.ifilter object at 0x000000000283DDA0>, <itertools.ifilter object at 0x000000000283DE80>, <itertools.ifilter object at 0x000000000284F080>]
it 的每次迭代都会产生另一个迭代器，这就是您的问题的原因。
所以你必须“迭代你的迭代器”：
import multiprocessing
from itertools import imap, ifilter
import sys
def test(t):
return 't = ' + str(t) # return value rather than printing
if __name__ == '__main__': # required for Windows
mp_pool = multiprocessing.Pool(multiprocessing.cpu_count())
it = imap(lambda x: ifilter(lambda y: x+y > 10, xrange(10)), xrange(10))
for the_iterator in it:
result = mp_pool.map(test, the_iterator)
print result
mp_pool.close() # needed to ensure all processes terminate
mp_pool.join() # needed to ensure all processes terminate
如您所定义的it，打印的结果是：
[]
[]
['t = 9']
['t = 8', 't = 9']
['t = 7', 't = 8', 't = 9']
['t = 6', 't = 7', 't = 8', 't = 9']
['t = 5', 't = 6', 't = 7', 't = 8', 't = 9']
['t = 4', 't = 5', 't = 6', 't = 7', 't = 8', 't = 9']
['t = 3', 't = 4', 't = 5', 't = 6', 't = 7', 't = 8', 't = 9']
['t = 2', 't = 3', 't = 4', 't = 5', 't = 6', 't = 7', 't = 8', 't = 9']
但是，如果您想充分利用多处理（假设您有足够的处理器），那么您可以使用 map_async 以便可以一次提交所有作业：
import multiprocessing
from itertools import imap, ifilter
import sys
def test(t):
return 't = ' + str(t) # return value rather than printing
if __name__ == '__main__': # required for Windows
mp_pool = multiprocessing.Pool(multiprocessing.cpu_count())
it = imap(lambda x: ifilter(lambda y: x+y > 10, xrange(10)), xrange(10))
results = [mp_pool.map_async(test, the_iterator) for the_iterator in it]
for result in results:
print result.get()
mp_pool.close() # needed to ensure all processes terminate
mp_pool.join() # needed to ensure all processes terminate
或者您可以考虑使用my_pool.imap，它与my_pool.map_async 不同，它不会首先将可迭代参数转换为列表以确定用于提交作业的最佳chunksize 值（阅读文档，它是不太好），但默认情况下使用 chunksize 值 1，这对于非常大的可迭代对象通常是不可取的：
results = [mp_pool.imap(test, the_iterator) for the_iterator in it]
for result in results:
print list(result) # to get a comparable printout as when using map_async
更新：使用多处理生成列表
import multiprocessing
from itertools import imap, ifilter
import sys
def test(t):
return 't = ' + str(t) # return value rather than printing
def generate_lists(x):
return list(ifilter(lambda y: x+y > 10, xrange(10)))
if __name__ == '__main__': # required for Windows
mp_pool = multiprocessing.Pool(multiprocessing.cpu_count())
lists = mp_pool.imap(generate_lists, xrange(10))
# lists, returned by mp_pool.imap, is an iterable
# as each element of lists becomes available it is passed to test:
results = mp_pool.imap(test, lists)
# as each result becomes available
for result in results:
print result
mp_pool.close() # needed to ensure all processes terminate
打印：
t = []
t = []
t = [9]
t = [8, 9]
t = [7, 8, 9]
t = [6, 7, 8, 9]
t = [5, 6, 7, 8, 9]
t = [4, 5, 6, 7, 8, 9]
t = [3, 4, 5, 6, 7, 8, 9]
t = [2, 3, 4, 5, 6, 7, 8, 9]