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. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/UpdateStateByKey-How-to-improve-performance-tp11575.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
