Kmeans Streaming process flow

[Chandra Mohan, Ananda Vel Murugan]

Hi,

I am in the process of implementing a spark streaming application to do 
clustering of some events. I have a DStream of vectors that I have created from 
each event. Now I am trying to apply clustering. I referred following example 
in spark github.

There is a train method and predictOnValues method. I am confused how to map 
this example for my use case. In my case, I would be getting the stream of 
events 24 * 7. I am not sure how to split the "all day" data separately for 
train and predict methods. Or should this streaming application be run in train 
mode for few days and predict mode later? I am not able to find a suitable 
example on the web. Please advise. Thanks.

https://github.com/apache/spark/blob/branch-1.2/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingKMeansExample.scala

object StreamingKMeansExample {

  def main(args: Array[String]) {
    if (args.length != 5) {
      System.err.println(
        "Usage: StreamingKMeansExample " +
          "<trainingDir> <testDir> <batchDuration> <numClusters> 
<numDimensions>")
      System.exit(1)
    }

    val conf = new 
SparkConf().setMaster("local").setAppName("StreamingKMeansExample")
    val ssc = new StreamingContext(conf, Seconds(args(2).toLong))

    val trainingData = ssc.textFileStream(args(0)).map(Vectors.parse)
    val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse)

    val model = new StreamingKMeans()
      .setK(args(3).toInt)
      .setDecayFactor(1.0)
      .setRandomCenters(args(4).toInt, 0.0)

    model.trainOn(trainingData)
    model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()

    ssc.start()
    ssc.awaitTermination()
  }
}

Regards,
Anand.C