To be honest, I never succeeded to execute the twitter streaming example with Zeppelin. Since the Scheduler is FIFO, once you launch the paragraph, it stays forever in pending state so I couldn't stop the stream apart from killing the zeppelin process.
So + 1 on the idea of streaming abstraction On Fri, Aug 28, 2015 at 5:17 PM, Amos B. Elberg <[email protected]> wrote: > Seems like we have two separate components. One is to allow interpreters > to update interpreter results. The other is interpreters that do that. > > > On Aug 28, 2015, at 11:02 AM, IT CTO <[email protected]> wrote: > > > > +1 for streaming support. > > > > בתאריך יום ו׳, 28 באוג׳ 2015, 17:53 מאת Christian Tzolov < > [email protected] > >> : > > > >> I've been exploring lately how would a streaming platforms fit into > >> Zeppelin. > >> > >> The streaming processes are continuous as opposed to the usual > Paragraph's > >> batch like jobs. > >> > >> For example you start the twitter search stream in a paragraph (by > clicking > >> the Run button) the underlying streaming platform will open a stream and > >> will return and the Paragraph will show a 'FINISHED' state while the > real > >> state in this case is 'STARTED' (or alike)? > >> > >> Currently Zeppelin doesn't seem to provide abstractions to track and > stop > >> such long-living tasks? > >> > >> I've checked the Spark Streaming tutorial but couldn't find an > explanation > >> of how to track and stop running streams? > >> > >> Do you think this is a valid use case for Zeppelin? Perhaps Spark > Streaming > >> and Flink have already solved this somehow? > >> > >> If you think this is valid use case i will open a JIRA ticket so we can > >> start looking for solutions. > >> > >> Cheers, > >> Christian > >> > >> -- > >> Christian Tzolov <http://www.linkedin.com/in/tzolov> | Solution > Architect, > >> EMEA Practice Team | Pivotal <http://pivotal.io/> > >> [email protected]|+31610285517 > >> >
