multiprocessing: map vs map_async
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 Poolimport timedef f(x): print x*xif __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:
01941625364964810HERE14MORE1625369496481DONEpool.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.