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I have a case class in Scala like this

case class RemoteCopyGroup(
  ts: Long,
  systemId: String,
  name: String,
  id: Int,
  role: String,
  mode: String,
   remoteGroupName: String)


object RemoteCopyGroup {

    // to be removed

   val arrayOfIds = Array("CZ210507H1", "CZ20030W4H", "CZ29400JBJ")
   def randomSerialNumber = Random.shuffle(arrayOfIds.toList).head

  def get(x: Rate): RemoteCopyGroup = {
    RemoteCopyGroup(
    x.timestamp.getTime / 1000,
    randomSerialNumber,
    Random.nextString(2),
    Random.nextInt(3),
    Random.nextString(2),
    Random.nextString(2),
    Random.nextString(2))
  }
}

I am generating a stream of data using RateStreamSource like this

val remoteCopyGroupDS: Dataset[(String, RemoteCopyGroup)] = sparkSession
  .readStream
  .format("rate") // <-- use RateStreamSource
  .option("rowsPerSecond", rate)
  .load()
  .as[Rate].filter(_.value % 10 == 0)
  .map(RemoteCopyGroup.get).map(rcg => rcg.systemId -> rcg)

I want to do stateful operations on remoteCopyGroupDS but I am not able to use methods like mapWithState because remoteCopyGroupDS is not a DStream. Is there a way I can generate a DStream that continuously emits data or I can convert current DataSet i.e. remoteCopyGroupDS to DStream ?

user9920500
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1 Answers1

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The KafkaRate is a stream of rate/price data that is continuously published by a Kafka Topic or a Kafka Topic Stream. In Spark, the DStream is a distributed stream of data that can be computed on the fly. DStreams represent a time series of data and are used to compute values for use in other Spark operations. DStreams can be created from a variety of sources including Kafka, Flume, HDFS, and many others. Spark provides a Receiver object to interface with Kafka sources. You can create Receiver objects from Kafka Sources using the created receiver method that is provided in the SparkContext.

George Harry
  • 11