Few more suggestions.
1. See the web ui, is the system running any jobs? If not, then you may
need to give the system more nodes. Basically the system should have more
cores than the number of receivers.
2. Furthermore there is a streaming specific web ui which gives more
streaming specific data.
On Fri, May 23, 2014 at 6:02 PM, Patrick Wendell <pwend...@gmail.com> wrote:
> Also one other thing to try, try removing all of the logic form inside
> of foreach and just printing something. It could be that somehow an
> exception is being triggered inside of your foreach block and as a
> result the output goes away.
>
> On Fri, May 23, 2014 at 6:00 PM, Patrick Wendell <pwend...@gmail.com>
> wrote:
> > Hey Jim,
> >
> > Do you see the same behavior if you run this outside of eclipse?
> >
> > Also, what happens if you print something to standard out when setting
> > up your streams (i.e. not inside of the foreach) do you see that? This
> > could be a streaming issue, but it could also be something related to
> > the way it's running in eclipse.
> >
> > - Patrick
> >
> > On Fri, May 23, 2014 at 2:57 PM, Jim Donahue <jdona...@adobe.com> wrote:
> >> I¹m trying out 1.0 on a set of small Spark Streaming tests and am
> running
> >> into problems. Here¹s one of the little programs I¹ve used for a long
> >> time ‹ it reads a Kafka stream that contains Twitter JSON tweets and
> does
> >> some simple counting. The program starts OK (it connects to the Kafka
> >> stream fine) and generates a stream of INFO logging messages, but never
> >> generates any output. :-(
> >>
> >> I¹m running this in Eclipse, so there may be some class loading issue
> >> (loading the wrong class or something like that), but I¹m not seeing
> >> anything in the console output.
> >>
> >> Thanks,
> >>
> >> Jim Donahue
> >> Adobe
> >>
> >>
> >>
> >> val kafka_messages =
> >> KafkaUtils.createStream[Array[Byte], Array[Byte],
> >> kafka.serializer.DefaultDecoder, kafka.serializer.DefaultDecoder](ssc,
> >> propsMap, topicMap, StorageLevel.MEMORY_AND_DISK)
> >>
> >>
> >> val messages = kafka_messages.map(_._2)
> >>
> >>
> >> val total = ssc.sparkContext.accumulator(0)
> >>
> >>
> >> val startTime = new java.util.Date().getTime()
> >>
> >>
> >> val jsonstream = messages.map[JSONObject](message =>
> >> {val string = new String(message);
> >> val json = new JSONObject(string);
> >> total += 1
> >> json
> >> }
> >> )
> >>
> >>
> >> val deleted = ssc.sparkContext.accumulator(0)
> >>
> >>
> >> val msgstream = jsonstream.filter(json =>
> >> if (!json.has("delete")) true else { deleted += 1; false}
> >> )
> >>
> >>
> >> msgstream.foreach(rdd => {
> >> if(rdd.count() > 0){
> >> val data = rdd.map(json => (json.has("entities"),
> >> json.length())).collect()
> >> val entities: Double = data.count(t => t._1)
> >> val fieldCounts = data.sortBy(_._2)
> >> val minFields = fieldCounts(0)._2
> >> val maxFields = fieldCounts(fieldCounts.size - 1)._2
> >> val now = new java.util.Date()
> >> val interval = (now.getTime() - startTime) / 1000
> >> System.out.println(now.toString)
> >> System.out.println("processing time: " + interval + " seconds")
> >> System.out.println("total messages: " + total.value)
> >> System.out.println("deleted messages: " + deleted.value)
> >> System.out.println("message receipt rate: " +
> (total.value/interval)
> >> + " per second")
> >> System.out.println("messages this interval: " + data.length)
> >> System.out.println("message fields varied between: " + minFields
> + "
> >> and " + maxFields)
> >> System.out.println("fraction with entities is " + (entities /
> >> data.length))
> >> }
> >> }
> >> )
> >>
> >> ssc.start()
> >>
>
