Dataframe code with take and show methods

package com.basan.day4.df

import org.apache.log4j._
import org.apache.spark._
import org.apache.spark.sql._
import org.apache.spark.sql.SparkSession

/**
 * 0 Will 33 385
 * 1 Jean-Luc 26 2
 * 2 Hugh 55 221
 * 3 Deanna 40 465
 * 4 Quark 68 21
 * 5 Weyoun 59 318
 * 6 Gowron 37 220
 * 7 Will 54 307*
 */

object DataFrameExampleSQL {

  case class Person(ID: Int, name: String, age: Int, numFriends: Int)

  def mapper(line: String): Person = {
    val fields = line.split(",")
    val person: Person = Person(fields(0).toInt, fields(1), fields(2).toInt, fields(3).toInt)
    person
  }

  def main(args: Array[String]) {

    Logger.getLogger("org").setLevel(Level.ERROR)

    val spark = SparkSession.builder
      .appName("Data frame test using SQL")
      .master("local[*]")
      //needed only in windows check it
      // .config("spark.sql.warehouse.dir", "/Users/basan/Documents/Spark/tempwarehouse")
      // .config("spark.driver.allowMultipleContexts", "true")
      .getOrCreate()

    // val sc = new SparkContext("local[*]", "ERROR count with bigFile")

    var lines = spark.sparkContext.textFile("/Users/basan/workspace-spark/sparkDemo1/spark-data/friends-data.csv")
    import spark.implicits._
    val schemaPeople = lines.map(mapper).toDS()

    schemaPeople.createOrReplaceTempView("people")

    //SQL on the dataframe

    val teenagers = spark.sql("SELECT * from people where age>=13 AND age<=19")
        print("show-----")

    teenagers.show(2)
    print("take-----")
    teenagers.take(2)

    val results = teenagers.collect()

    results.foreach(println)

    scala.io.StdIn.readLine()

    spark.stop()

  }
 
 
  //look into the spark UI and SQL tab observe WholeStageCodegen block is generated
  //Check the plan to see how queries gets executed and tuning can be done using [plans]
 
 

}