build.sbt

val AwsBuild = true

organization :="com.github.aleciop"

name := "dbscan-distributed"

version :="1.0-SNAPSHOT"

developers ++= List(
  Developer("AlecioP", "Alessio Portaro", "@AlecioP", url("https://github.com/AlecioP")),
  Developer("andreaErco", "Andrea Ercolessi", "@andreaErco", url("https://github.com/andreaErco"))
)

//Emr-5.32.0 uses Spark 2.4.7 which in turn uses Scala 2.11.12
//Run spark-shell on cluster driver to find out


if(AwsBuild){
  //FOR AWS EXECUTION
  scalaVersion := "2.11.12"
}else{
  //FOR LOCAL EXECUTION
  scalaVersion := "2.12.13"
}

val sparkVersion = "2.4.7"

/*
https://mungingdata.com/apache-spark/building-jar-sbt/

" 
[...]
The “provided” string at the end of the line
indicates that the spark-sql dependency should 
be provided by the runtime environment that uses 
this JAR file.
[...]
*/

// https://mvnrepository.com/artifact/org.apache.spark/spark-core
libraryDependencies += "org.apache.spark" %% "spark-core" % sparkVersion % "provided"


//https://mungingdata.com/apache-spark/building-jar-sbt/
//For JAR naming conventions


artifactName := { (sv: ScalaVersion, module: ModuleID, artifact: Artifact) =>
  artifact.name + "_" + sv.binary + "-" + sparkVersion + "_" + module.revision + "." + artifact.extension
}


/*
  Usually if you run the scala application as a standalone app
  which is packed in a jar file, you should link or directly
  compile within the jar the scala-lang jar in order to allow JVM 
  to understand scala code
  Anyway, since we execute our application through SPARK-SUBMIT
  then this latter should already have a scala setup in it

  INFO reference again from : 
  https://mungingdata.com/apache-spark/building-jar-sbt/

  val classPath = Seq(
    "scala-library-2.11.12.jar"
  )

  packageOptions += Package.ManifestAttributes(
    "Class-Path" -> classPath.mkString(" ")
  )
*/