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 parameter s don't apply to function objects in scala.

user6910411 gave me a hint by using ClassTag s 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.