That was it. Thanks Akhil and Owen for your quick response.

On Sat, Jan 17, 2015 at 4:27 AM, Sean Owen <so...@cloudera.com> wrote:

> Not print(kafkaStream), which would just print some String description
> of the stream to the console, but kafkaStream.print(), which actually
> invokes the print operation on the stream.
>
> On Sat, Jan 17, 2015 at 10:17 AM, Rohit Pujari <rpuj...@hortonworks.com>
> wrote:
> > Hi Francois:
> >
> > I tried using "print(kafkaStream)” as output operator but no luck. It
> throws
> > the same error. Any other thoughts?
> >
> > Thanks,
> > Rohit
> >
> >
> > From: "francois.garil...@typesafe.com" <francois.garil...@typesafe.com>
> > Date: Saturday, January 17, 2015 at 4:10 AM
> > To: Rohit Pujari <rpuj...@hortonworks.com>
> > Subject: Re: Spark Streaming
> >
> > Streams are lazy. Their computation is triggered by an output operator,
> > which is apparently missing from your code. See the programming guide:
> >
> >
> https://spark.apache.org/docs/latest/streaming-programming-guide.html#output-operations-on-dstreams
> >
> > —
> > FG
> >
> >
> > On Sat, Jan 17, 2015 at 11:06 AM, Rohit Pujari <rpuj...@hortonworks.com>
> > wrote:
> >>
> >> Hello Folks:
> >>
> >> I'm running into following error while executing relatively straight
> >> forward spark-streaming code. Am I missing anything?
> >>
> >> Exception in thread "main" java.lang.AssertionError: assertion failed:
> No
> >> output streams registered, so nothing to execute
> >>
> >>
> >> Code:
> >>
> >> val conf = new SparkConf().setMaster("local[2]").setAppName("Streams")
> >>     val ssc = new StreamingContext(conf, Seconds(1))
> >>
> >>     val kafkaStream = {
> >>       val sparkStreamingConsumerGroup = "spark-streaming-consumer-group"
> >>       val kafkaParams = Map(
> >>         "zookeeper.connect" ->
> "node1.c.emerald-skill-783.internal:2181",
> >>         "group.id" -> "twitter",
> >>         "zookeeper.connection.timeout.ms" -> "1000")
> >>       val inputTopic = "twitter"
> >>       val numPartitionsOfInputTopic = 2
> >>       val streams = (1 to numPartitionsOfInputTopic) map { _ =>
> >>         KafkaUtils.createStream(ssc, kafkaParams, Map(inputTopic -> 1),
> >> StorageLevel.MEMORY_ONLY_SER)
> >>       }
> >>       val unifiedStream = ssc.union(streams)
> >>       val sparkProcessingParallelism = 1
> >>       unifiedStream.repartition(sparkProcessingParallelism)
> >>     }
> >>
> >>     //print(kafkaStream)
> >>     ssc.start()
> >>     ssc.awaitTermination()
> >>
> >> --
> >> Rohit Pujari
> >>
> >>
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-- 
Rohit Pujari
Solutions Engineer, Hortonworks
rpuj...@hortonworks.com
716-430-6899

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