Hi When you say "Zeppelin and STS", I am assuming you mean "Spark Interpreter" and "JDBC interpreter" respectively.
Through Zeppelin, you can either run your own spark application (by using Zeppelin's own spark context) using spark interpreter OR you can access STS, which is a spark application ie separate Spark Context using JDBC interpreter. There should not be any need for these 2 contexts to coexist. If you want to share data, save it to hive from either context, and you should be able to see the data from other context. Best Ayan On Mon, Jul 11, 2016 at 3:00 PM, Chanh Le <giaosu...@gmail.com> wrote: > Hi Ayan, > I tested It works fine but one more confuse is If my (technical) users > want to write some code in zeppelin to apply thing into Hive table? > Zeppelin and STS can’t share Spark Context that mean we need separated > process? Is there anyway to use the same Spark Context of STS? > > Regards, > Chanh > > > On Jul 11, 2016, at 10:05 AM, Takeshi Yamamuro <linguin....@gmail.com> > wrote: > > Hi, > > ISTM multiple sparkcontexts are not recommended in spark. > See: https://issues.apache.org/jira/browse/SPARK-2243 > > // maropu > > > On Mon, Jul 11, 2016 at 12:01 PM, ayan guha <guha.a...@gmail.com> wrote: > >> Hi >> >> Can you try using JDBC interpreter with STS? We are using Zeppelin+STS on >> YARN for few months now without much issue. >> >> On Mon, Jul 11, 2016 at 12:48 PM, Chanh Le <giaosu...@gmail.com> wrote: >> >>> Hi everybody, >>> We are using Spark to query big data and currently we’re using Zeppelin >>> to provide a UI for technical users. >>> Now we also need to provide a UI for business users so we use Oracle BI >>> tools and set up a Spark Thrift Server (STS) for it. >>> >>> When I run both Zeppelin and STS throw error: >>> >>> INFO [2016-07-11 09:40:21,905] ({pool-2-thread-4} >>> SchedulerFactory.java[jobStarted]:131) - Job >>> remoteInterpretJob_1468204821905 started by scheduler >>> org.apache.zeppelin.spark.SparkInterpreter835015739 >>> INFO [2016-07-11 09:40:21,911] ({pool-2-thread-4} >>> Logging.scala[logInfo]:58) - Changing view acls to: giaosudau >>> INFO [2016-07-11 09:40:21,912] ({pool-2-thread-4} >>> Logging.scala[logInfo]:58) - Changing modify acls to: giaosudau >>> INFO [2016-07-11 09:40:21,912] ({pool-2-thread-4} >>> Logging.scala[logInfo]:58) - SecurityManager: authentication disabled; ui >>> acls disabled; users with view permissions: Set(giaosudau); users with >>> modify permissions: Set(giaosudau) >>> INFO [2016-07-11 09:40:21,918] ({pool-2-thread-4} >>> Logging.scala[logInfo]:58) - Starting HTTP Server >>> INFO [2016-07-11 09:40:21,919] ({pool-2-thread-4} >>> Server.java[doStart]:272) - jetty-8.y.z-SNAPSHOT >>> INFO [2016-07-11 09:40:21,920] ({pool-2-thread-4} >>> AbstractConnector.java[doStart]:338) - Started >>> SocketConnector@0.0.0.0:54818 >>> INFO [2016-07-11 09:40:21,922] ({pool-2-thread-4} >>> Logging.scala[logInfo]:58) - Successfully started service 'HTTP class >>> server' on port 54818. >>> INFO [2016-07-11 09:40:22,408] ({pool-2-thread-4} >>> SparkInterpreter.java[createSparkContext]:233) - ------ Create new >>> SparkContext local[*] ------- >>> WARN [2016-07-11 09:40:22,411] ({pool-2-thread-4} >>> Logging.scala[logWarning]:70) - Another SparkContext is being constructed >>> (or threw an exception in its constructor). This may indicate an error, >>> since only one SparkContext may be running in this JVM (see SPARK-2243). >>> The other SparkContext was created at: >>> >>> Is that mean I need to setup allow multiple context? Because It’s only >>> test in local with local mode If I deploy on mesos cluster what would >>> happened? >>> >>> Need you guys suggests some solutions for that. Thanks. >>> >>> Chanh >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>> >>> >> >> >> -- >> Best Regards, >> Ayan Guha >> > > > > -- > --- > Takeshi Yamamuro > > > -- Best Regards, Ayan Guha
