Scala Spark demo of joining multiple dataframes on same columns using implicit classes. git clone then run using `sbt run`

.gitignore

project
target
metastore_db
derby.log

build.sbt

scalaVersion := "2.11.12"
libraryDependencies += "org.apache.spark" %% "spark-core" % "2.3.0"
libraryDependencies += "org.apache.spark" %% "spark-sql" % "2.3.0"
libraryDependencies += "org.apache.spark" %% "spark-hive" % "2.3.0"

script.scala

import org.apache.spark.SparkConf
import org.apache.spark.sql.{DataFrame, SparkSession}
import java.time.LocalDate
object DataFrameExtensions_ {
  implicit class DataFrameExtensions(df: DataFrame){
    def featuresGroup1(groupBy: Seq[String], asAt: LocalDate): DataFrame = {df}
    def featuresGroup2(groupBy: Seq[String], asAt: LocalDate): DataFrame = {df}
  }
  implicit class SequenceOfDataFrames(seqDF: Seq[DataFrame]){
    def joinDataFramesOnColumns(joinColumns: Seq[String]) : DataFrame = {
      if (joinColumns.isEmpty) {
        return seqDF.reduce(_.crossJoin(_))
      }
      seqDF.reduce(_.join(_, joinColumns))
    }
  }
}
object Application extends App{
  System.setSecurityManager(null)  
  import DataFrameExtensions_._
  val spark = SparkSession.builder().config(new SparkConf().setMaster("local[*]")).enableHiveSupport().getOrCreate()
  import spark.implicits._
  val df = Seq((8, "bat"),(64, "mouse"),(-27, "horse")).toDF("number", "word")
  val groupBy = Seq("number", "word")
  val asAt = LocalDate.now()
  val dataFrames = Seq(df.featuresGroup1(_,_), df.featuresGroup2(_,_)).map(_(groupBy, asAt))
  //Another way of achieving the same:
  //val funcs:List[DataFrame=>(Seq[String], java.time.LocalDate) => org.apache.spark.sql.DataFrame]  = List(_.featuresGroup1, _.featuresGroup2)
  //val dataFrames = funcs.map(x => x(df)(groupBy, asAt))
  dataFrames.joinDataFramesOnColumns(groupBy).show
  spark.stop()
}