Idea is to use a heap and get topK elements from every partition...then use aggregateBy and for combOp do a merge routine from mergeSort...basically get 100 items from partition 1, 100 items from partition 2, merge them so that you get sorted 200 items and take 100...for merge you can use heap as well...Matei had a BPQ inside Spark which we use all the time...Passing arrays over wire is better than passing full heap objects and merge routine on array should run faster but needs experiment...
On Thu, Mar 26, 2015 at 9:26 PM, Aung Htet <aung....@gmail.com> wrote: > Hi Debasish, > > Thanks for your suggestions. In-memory version is quite useful. I do not > quite understand how you can use aggregateBy to get 10% top K elements. Can > you please give an example? > > Thanks, > Aung > > On Fri, Mar 27, 2015 at 2:40 PM, Debasish Das <debasish.da...@gmail.com> > wrote: > >> You can do it in-memory as well....get 10% topK elements from each >> partition and use merge from any sort algorithm like timsort....basically >> aggregateBy >> >> Your version uses shuffle but this version is 0 shuffle..assuming your >> data set is cached you will be using in-memory allReduce through >> treeAggregate... >> >> But this is only good for top 10% or bottom 10%...if you need to do it >> for top 30% then may be the shuffle version will work better... >> >> On Thu, Mar 26, 2015 at 8:31 PM, Aung Htet <aung....@gmail.com> wrote: >> >>> Hi all, >>> >>> I have a distribution represented as an RDD of tuples, in rows of >>> (segment, score) >>> For each segment, I want to discard tuples with top X percent scores. >>> This seems hard to do in Spark RDD. >>> >>> A naive algorithm would be - >>> >>> 1) Sort RDD by segment & score (descending) >>> 2) Within each segment, number the rows from top to bottom. >>> 3) For each segment, calculate the cut off index. i.e. 90 for 10% cut >>> off out of a segment with 100 rows. >>> 4) For the entire RDD, filter rows with row num <= cut off index >>> >>> This does not look like a good algorithm. I would really appreciate if >>> someone can suggest a better way to implement this in Spark. >>> >>> Regards, >>> Aung >>> >> >> >
