Simplest approach would be to push the streaming data (after the computations) to a SQL-Like DB and then let your visualization piece pull it from the DB. Another approach would be to make your visualization piece a web-socket (If you are using D3JS etc) and then from your streaming application you can push the data to the web-socket which will power the dashboards.
Thanks Best Regards On Fri, Oct 2, 2015 at 5:17 PM, Sureshv <suresh.ku...@transerainc.com> wrote: > Hi, > > I am new to Spark and I would like know how to compute (dynamically) > real-time visualizations using Spark streaming (Kafka). > > Use case : We have Real-time analytics dashboard (reports and dashboard), > user can define report (visualization) with certain parameters like, > refresh > period, choose various metrics (segment variables & profile variables). > > We should compute only visualizations those are in use (users are > accessing) > with events coming from kafka streams using Spark streaming. > > Solution : One way of doing is compute visualizations for every incoming > message and write back into result streams and application which consume > the > processed data/result streams. > > I would like to know is there any better approach? Please advice me here. > > Thanks, > Suresh > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Compute-Real-time-Visualizations-using-spark-streaming-tp24908.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 > >
