multiprocessing: map vs map_async

What's the difference between using map and map_async? Are they not running the same function after distributing the items from the list to 4 processes?

So is it wrong to presume both are running asynchronous and parallel?

def f(x):
return 2*x


p=Pool(4)
l=[1,2,3,4]
out1=p.map(f,l)
#vs
out2=p.map_async(f,l)
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最佳答案

There are four choices to mapping jobs to processes. You have to consider multi-args, concurrency, blocking, and ordering. map and map_async only differ with respect to blocking. map_async is non-blocking where as map is blocking

So let's say you had a function

from multiprocessing import Pool
import time


def f(x):
print x*x


if __name__ == '__main__':
pool = Pool(processes=4)
pool.map(f, range(10))
r = pool.map_async(f, range(10))
# DO STUFF
print 'HERE'
print 'MORE'
r.wait()
print 'DONE'

Example output:

0
1
9
4
16
25
36
49
64
81
0
HERE
1
4
MORE
16
25
36
9
49
64
81
DONE

pool.map(f, range(10)) will wait for all 10 of those function calls to finish so we see all the prints in a row. r = pool.map_async(f, range(10)) will execute them asynchronously and only block when r.wait() is called so we see HERE and MORE in between but DONE will always be at the end.


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