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Issue #12708: Add starmap() and starmap_async() methods (similar to itertools.starmap()) to multiprocessing.Pool.

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Patch by Hynek Schlawack.
This commit is contained in:
Antoine Pitrou 2011-12-21 11:03:24 +01:00
parent 12f65d1fef
commit de911b2915
6 changed files with 72 additions and 3 deletions
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32
Lib/multiprocessing/pool.py
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@ -64,6 +64,9 @@ job_counter = itertools.count()
def mapstar(args):
return list(map(*args))
def starmapstar(args):
return list(itertools.starmap(args[0], args[1]))
#
# Code run by worker processes
#
@ -248,7 +251,25 @@ class Pool(object):
in a list that is returned.
'''
assert self._state == RUN
return self.map_async(func, iterable, chunksize).get()
return self._map_async(func, iterable, mapstar, chunksize).get()
def starmap(self, func, iterable, chunksize=None):
'''
Like `map()` method but the elements of the `iterable` are expected to
be iterables as well and will be unpacked as arguments. Hence
`func` and (a, b) becomes func(a, b).
'''
assert self._state == RUN
return self._map_async(func, iterable, starmapstar, chunksize).get()
def starmap_async(self, func, iterable, chunksize=None, callback=None,
error_callback=None):
'''
Asynchronous version of `starmap()` method.
'''
assert self._state == RUN
return self._map_async(func, iterable, starmapstar, chunksize,
callback, error_callback)
def imap(self, func, iterable, chunksize=1):
'''
@ -302,6 +323,13 @@ class Pool(object):
Asynchronous version of `map()` method.
'''
assert self._state == RUN
return self._map_async(func, iterable, mapstar, chunksize)
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 not hasattr(iterable, '__len__'):
iterable = list(iterable)
@ -315,7 +343,7 @@ class Pool(object):
task_batches = Pool._get_tasks(func, iterable, chunksize)
result = MapResult(self._cache, chunksize, len(iterable), callback,
error_callback=error_callback)
self._taskqueue.put((((result._job, i, mapstar, (x,), {})
self._taskqueue.put((((result._job, i, mapper, (x,), {})
for i, x in enumerate(task_batches)), None))
return result