Generic state management

Question

This question is a follow-up to State management not serializable.

---

I want to encapsulate state management logic.

The following represents where I am at right now:

class StateManager(
  stream: DStream[(String, String)],
  updateStateFunction: (String, Option[String], State[String]) => Option[(String, String)]
) {
  lazy val myState = stream.mapWithState(stateSpec).map(_.get)
  lazy val stateSpec = StateSpec.function(updateStateFunction)
}

object StateManager {
  def apply(
    _dStream: DStream[(String, String)],
    _updateState: (String, Option[String], State[String]) => Option[(String, String)]
  ) =
    new StateManager(dStream, updateState)
}

This works fine, but only allows DStream[(String,String)] to be handled, which is a first step towards generic state management, fit to welcome any DStream: from DStream[(Int,String)] to DStream[(String,myCustomClass)].

myState requires to be a value function in order to work (serialization).

But I face a problem as type parameters don't apply to function objects in scala.

user6910411 gave me a hint by using ClassTags with an enclosing method (Type-parameterize a DStream), but in turn it'd still be a method.

Would anyone have some intel on how to overcome those difficulties?

---

The context:

Spark 1.6

Spark Graph:

object Consumer_Orchestrator {
    def main(args: Array[String]) = {
        //setup configurations

        val streamingContext = StreamingEnvironment(/*configurations*/)

        val kafkaStream = streamingContext.stream()

        val updateStateFunction: (String, Option[String], State[String]) => Option[(String, String)] = (key, value, state) => {/*some code*/}
        val initialState = emptyRDD

        val stateManager = StateManager(kafkaStream, updateState)
        val state: DStream[(String, String)] = stateManager.myState

        state.foreachRDD(_.foreach(println))

        myStreamingContext.start()
        myStreamingContext.awaitTermination()
    }
}

The StreamingEnvironment class to create the Streaming:

class StreamingEnvironment(sparkConf: SparkConf, kafkaConf: KafkaConf) {
    val sparkContext = spark.SparkContext.getOrCreate(sparkConf)
    lazy val streamingContext = new StreamingContext(sparkContext, Seconds(30))

    mStreamingContext.checkpoint(/*directory checkpoint*/)
    mStreamingContext.remember(Minutes(1))

    def stream() = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](streamingContext, myKafkaConf.mBrokers, myKafkaConf.mTopics)
    def stop() = sparkContext.stop()
}

object StreamingEnvironment {
    def apply(kafkaConf: KafkaConf) = {
    val sparkConf = new SparkConf

    new StreamingEnvironment(sparkConf, kafkaConf)
    }
}

Answer 1

Here you are:

• App.scala:

  import org.apache.spark.{SparkContext, SparkConf}
  import org.apache.spark.streaming._
  import org.apache.spark.streaming.dstream.ConstantInputDStream
  import statemanager._
  
  object App {
    def main(args: Array[String]): Unit = {
      val sc = new SparkContext("local[*]", "generic", new SparkConf())
      val ssc = new StreamingContext(sc, Seconds(10))
      ssc.checkpoint("/tmp/chk")
  
      StateManager(
        new ConstantInputDStream(ssc, sc.parallelize(Seq(("a", 1), ("b",2)))),
        (_: String, _: Option[Int], _: State[Int]) =>  Option(1)
      ).myState.print
      ssc.start()
      ssc.awaitTermination()
    }
  }
  

• StateManage.scala:

  package statemanager
  
  import scala.reflect.ClassTag
  import org.apache.spark.streaming.{State, StateSpec}
  import org.apache.spark.streaming.dstream.DStream
  
  class StateManager[T : ClassTag, U : ClassTag, V : ClassTag, W : ClassTag](
    stream: DStream[(T, U)],
    updateStateFunction: (T, Option[U], State[V]) => Option[W]
  ) {
    lazy val myState = stream.mapWithState(stateSpec).map(_.get)
    lazy val stateSpec = StateSpec.function(updateStateFunction)
  }
  
  object StateManager {
    def apply[T : ClassTag, U : ClassTag, V : ClassTag, W : ClassTag](
      _dStream: DStream[(T, U)],
      _updateState: (T, Option[U], State[V]) => Option[W]
    ) =
      new StateManager(_dStream, _updateState)
  }
  

• build.sbt:

  scalaVersion := "2.11.8"
  
  val sparkVersion = "2.1.0"
  
  libraryDependencies ++= Seq(
    "org.apache.spark" %% "spark-core" % sparkVersion,
    "org.apache.spark" %% "spark-streaming" % sparkVersion
  )
  

• Directory structure:

  ├── App.scala
  ├── build.sbt
  └── StateManage.scala
  

• Example execution:

  sbt run
  ...
  -------------------------------------------
   Time: 1483701790000 ms
   -------------------------------------------
  1
  1
  ...
  

As you can see there is no magic here. If you introduce generic arguments you need ClassTags in the same context.