Log analyzer spark-scala code

LogSchema.scala

package com.demo.loganalyzer
/**
  *
  * Created by Sreejith Pillai.
  *
  * */
import org.apache.spark.Logging
import org.apache.spark.rdd.RDD
case class LogSchema(address: String,
                     datetime: String,
                     action: Option[String]
                    )

class TransformMapper extends  Logging{
  def transform(events: RDD[LogSchema]) = {
    val e = events.map(x => (x.datetime, 1)).reduceByKey (_ + _)
    e.saveAsTextFile("/user/sreejith/loganalyzer/logoutput/")
  }
}

object MapRawData extends Serializable with  Logging{
  def mapRawLine(line: String): Option[LogSchema] = {
    try {
      val fields = line.split(",", -1).map(_.trim)
      Some(
        LogSchema(
          address = fields(0),
          datetime = fields(1).substring(13, 15),
          action = if (fields(2).length > 2) Some(fields(2)) else None
        )
      )
    }
    catch {
      case e: Exception =>
        log.warn(s"Unable to parse line: $line")
        None
    }
  }
}

RunJob.scala

package com.demo.loganalyzer

import org.apache.spark._
import org.joda.time.DateTime
/**
  *
  * Created by Sreejith Pillai.
  *
  **/
object RunMainJob extends TransformMapper with Logging{
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("spark-loganalyzer")
    val sc = new SparkContext(conf)
    val startTimeJob = new DateTime(sc.startTime)
    val applicationId = sc.applicationId
    log.info("Application launched with id : " + applicationId)

    val rawData = sc.textFile("/user/sreejith/testlog")
    val numberOfRawLines = rawData.count()
    log.info("Number of lines to parse : " + numberOfRawLines)

    val mapRawData = MapRawData
    val parseData = rawData.flatMap(x => mapRawData.mapRawLine(x))
    log.info("Number of lines after parsing: ")

    transform(parseData)
  }
}