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From Aaron Dossett <aaron.doss...@target.com>
Subject Efficiently doing an analysis with Cartesian product (pyspark)
Date Mon, 23 Jun 2014 21:29:21 GMT
I am relatively new to Spark and am getting stuck trying to do the following:

- My input is integer key, value pairs where the key is not unique.  I'm
interested in information about all possible distinct key combinations, thus
the Cartesian product.
- My first attempt was to create a separate RDD of this cartesian product
and then use map() to calculate the data.  However, I was trying to pass
another RDD to the function map was calling, which I eventually figured out
was causing a run time error, even if the function I called with map did
nothing.  Here's a simple code example:

-------
def somefunc(x, y, RDD):
  return 0

input = sc.parallelize([(1,100), (1,200), (2, 100), (2,300)])

#Create all pairs of keys, including self-pairs
itemPairs = input.map(lambda x: x[0]).distinct()
itemPairs = itemPairs.cartesian(itemPairs)

print itemPairs.collect()

TC = itemPairs.map(lambda x: (x, somefunc(x[0], x[1], input)))

print TC.collect()
------

I'm assuming this isn't working because it isn't a very Spark-like way to do
things and I could imagine that passing RDDs into other RDD's map functions
might not make sense.  Could someone suggest to me a way to apply
transformations and actions to "input" that would produce a mapping of key
pairs to some information related to the values.

For example, I might want to (1, 2) to map to the sum of the maximum values
found for each key in the input (500 in my sample data above).  Extending
that example (1,1) would map to 300 and (2,2) to 400.

Please let me know if I should provide more details or a more robust
example.

Thank you, Aaron



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