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From Venkat Subramanian <vsubr...@gmail.com>
Subject UpdateStateByKey - How to improve performance?
Date Wed, 06 Aug 2014 20:29:55 GMT
The method

def updateStateByKey[S: ClassTag] ( updateFunc: (Seq[V], Option[S]) =>
Option[S] ): DStream[(K, S)]

takes Dstream (K,V) and Produces DStream (K,S)  in Spark Streaming

We have a input Dstream(K,V) that has 40,000 elements. We update on average
of 1000  elements of them in every 3 second batch, but based on how this
updateStateByKey function is defined, we are looping through 40,000 elements
(Seq[V]) to make an update for just 1000 elements and not updating 39000
elements. I think looping through extra 39000 elements is a waste of
performance.

Isn't there a better way to update this efficiently by just figuring out the
a hash map for the 1000 elements that are required to be updated and just
updating it (without looping through the unwanted elements)?  Shouldn't
there be a Streaming update function provided that updates selective members
or are we missing some concepts here?

I think updateStateByKey may be causing lot of performance degradation in
our app as we keep doing this again and again for every batch. Please let us
know if my thought process is correct here.




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