How to efficiently find top-k elements?

Question

I have a big sequence file storing the tfidf values for documents. Each line represents line and the columns are the value of tfidfs for each term (the row is a sparse vector). I'd like to pick the top-k words for each document using Hadoop. The naive solution is to loop through all the columns for each row in the mapper and pick the top-k but as the file becomes bigger and bigger I don't think this is a good solution. Is there a better way to do that in Hadoop?

Answer 1

 1. In every map calculate TopK (this is local top K for each map)
 2. Spawn a signle reduce , now top K from all mappers will flow to this reducer and hence global Top K will be evaluated. 

Think of the problem as

 1. You have been given the results of X number of horse races. 
 2. You need to find Top N fastest horse.