PySpark broadcast variables from local functions

Question

I'm attempting to create broadcast variables from within Python methods (trying to abstract some utility methods I'm creating that rely on distributed operations). However, I can't seem to access the broadcast variables from within the Spark workers.

Let's say I have this setup:

def main():
    sc = SparkContext()
    SomeMethod(sc)

def SomeMethod(sc):
    someValue = rand()
    V = sc.broadcast(someValue)
    A = sc.parallelize().map(worker)

def worker(element):
    element *= V.value  ### NameError: global name 'V' is not defined ###

However, if I instead eliminate the SomeMethod() middleman, it works fine.

def main():
    sc = SparkContext()
    someValue = rand()
    V = sc.broadcast(someValue)
    A = sc.parallelize().map(worker)

def worker(element):
    element *= V.value   # works just fine

I'd rather not have to put all my Spark logic in the main method, if I can. Is there any way to broadcast variables from within local functions and have them be globally visible to the Spark workers?

Alternatively, what would be a good design pattern for this kind of situation--e.g., I want to write a method specifically for Spark which is self-contained and performs a specific function I'd like to re-use?

Answer 1

I am not sure I completely understood the question but, if you need the V object inside the worker function you then you definitely should pass it as a parameter, otherwise the method is not really self-contained:

def worker(V, element):
    element *= V.value

Now in order to use it in map functions you need to use a partial, so that map only sees a 1 parameter function:

from functools import partial

def SomeMethod(sc):
    someValue = rand()
    V = sc.broadcast(someValue)
    A = sc.parallelize().map(partial(worker, V=V))