Thanks Matteo, this should work!
-Surender
On Thursday, 12 April, 2018, 1:13:38 PM IST, Matteo Cossu
<elco...@gmail.com> wrote:
I don't think it's trivial. Anyway, the naive solution would be a cross join
between user x items. But this can be very very expensive. I've encountered
once a similar problem, here how I solved it:
- create a new RDD with (itemID, index) where the index is a unique integer
between 0 and the number of items
- for every user sample n items by generating randomly n distinct integers
between 0 and the number of items (e.g. with rand.randint()), so you have a new
RDD (userID, [sample_items])
- flatten all the list in the previously created RDD and join them back with
the RDD with (itemID, index) using index as join attribute
You can do the same things with DataFrame using UDFs.
On 11 April 2018 at 23:01, surender kumar <skiit...@yahoo.co.uk> wrote:
right, this is what I did when I said I tried to persist and create an RDD out
of it to sample from. But how to do for each user?You have one rdd of users on
one hand and rdd of items on the other. How to go from here? Am I missing
something trivial?
On Thursday, 12 April, 2018, 2:10:51 AM IST, Matteo Cossu
<elco...@gmail.com> wrote:
Why broadcasting this list then? You should use an RDD or DataFrame. For
example, RDD has a method sample() that returns a random sample from it.
On 11 April 2018 at 22:34, surender kumar <skiit...@yahoo.co.uk.invalid> wrote:
I'm using pySpark.I've list of 1 million items (all float values ) and 1
million users. for each user I want to sample randomly some items from the item
list.Broadcasting the item list results in Outofmemory error on the driver,
tried setting driver memory till 10G. I tried to persist this array on disk
but I'm not able to figure out a way to read the same on the workers.
Any suggestion would be appreciated.
