We have a sophisticated Spark Streaming application that we have been using successfully in production for over a year to process a time series of events. Our application makes novel use of updateStateByKey() for state management.
We now have the need to perform exactly the same processing on input data that's not real-time, but has been persisted to disk. We do not want to rewrite our Spark Streaming app unless we have to. /Might it be possible to perform "large batches" processing on HDFS time series data using Spark Streaming?/ 1.I understand that there is not currently an InputDStream that could do what's needed. I would have to create such a thing. 2. Time is a problem. I would have to use the timestamps on our events for any time-based logic and state management 3. The "batch duration" would become meaningless in this scenario. Could I just set it to something really small (say 1 second) and then let it "fall behind", processing the data as quickly as it could? It all seems possible. But could Spark Streaming work this way? If I created a DStream that delivered (say) months of events, could Spark Streaming effectively process this in a "batch" fashion? Any and all comments/ideas welcome! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Use-Spark-Streaming-for-Batch-tp21745.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
