Top Customers facing frequent call drops in Roaming Analysis by Scala and Spark

Problem:
You will have a CDR (Call Details Record) file, you need to find out top customers facing frequent call drops in Roaming. This is a very important report which telecom companies use to prevent customer churn out, by calling them back and at the same time contacting their roaming partners to improve the connectivity issues in specific areas.

Sol.
package com.ravi.cdr

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext

object CallDetailsProblem {
  def main(args:Array[String]){
 
//First we’ll read the data from the csv file
    val sc = new SparkContext(new   SparkConf().setAppName("CallDetailsProblem ").setMaster("local[2]"))
    val logFile = "/home/om_workspace/test.csv"
    val text = sc.textFile(logFile)
 
//As we’re dealing with a CSV file with no headers it’s a good idea to define a case class that defines the schema
   //define the schema using a case class
 
    case class Call(visitor_locn: String, call_duration:
     Integer, phone_no: String, error_code: String)

     //Then create a RDD of Calls
     val calls = text.map(_.split(",")).map(p =>
     Call(p(0),p(1).toInt,p(2),p(3)))
   
    println(calls.count());
    calls.foreach {
      x =>  println(x)
      }
 
     var result = calls.map(x => (x.visitor_locn,1)).reduceByKey(_+_).collect.sortBy(_._2);
   
   // println(result.reverse.mkString("\n"));
    //Number of different customers having errors:
    var result2 = calls.map(x => (x.error_code,1)).reduceByKey(_+_).collect.sortBy(_._2);
    println(result2.reverse.mkString("\n"));
 
  }


}