Support for multithreading. about pydash HOT 1 OPEN

I was thinking about this earlier today. Though possible (and significantly benificial) to identify chain sections that do not depend on each other and perform those parts in parallel, that might be very challenging. What might be simpler is to provide a parallel API for the map functions, where the pydash.collections.itermap function is replaced with a threaded_map function.

Something maybe like:

import multiprocessing
pool = multiprocessing.Pool()

def pooled_iter_map(collection, iteratee):
    return pool.map(collection, iteratee)

def pooled_map(collection, iteratee):
    return list(pooled_iter_map(collection, iteratee))

def pooled_flat_map(collection, iteratee=None):
    return pyd.flatten(pooled_iter_map(collection, iteratee=iteratee))

from pydash.