ClassNotFoundException when using Mesos

SparkExamplesMinimal.scala

package spark

import org.apache.spark
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkConf

object SparkExamplesMinimal {
  def asInt(s: String): Option[Int] = {
    try {
      Some(Integer.parseInt(s))
    } catch {
      case _ : Throwable => None
    }
  }

  def main (args: Array[String]) {
    // configure Spark environment
    val conf = new SparkConf()
      .setMaster("mesos://mesos10-1:5050/")
      .setAppName("My app")
      .set("spark.executor.uri", "http://hdp8:8001/spark-1.0.0-SNAPSHOT.tar.gz")
    val sc = new spark.SparkContext(conf)

    // simulate "reading from a file" by converting a standard Scala list
    // comprehension to an RDD
    val fileData = for (i <- 1 to 1000000) yield {
      "%d\t%s".format(i, i)
    }
    val file: RDD[String] = sc.parallelize(fileData)
    println("count: " + file.count())  // THIS WORKS AND PRINTS 1000000

    // issue a `map()` on this RDD to convert string rows into integer tuples
    // while skipping failures
    val data: RDD[(Int, Int)] = file.flatMap(row => {
      val intRow = row.split('\t').map(asInt(_)).toList
      intRow match {
        case Some(key) :: Some(value) :: Nil =>
          Some((key, value))
        case _ =>
          None
      }
    })

    println("count: " + data.count())  // THIS GIVES ClassNotFoundException: spark.SparkExamplesMinimal$$anonfun$2
  }
}

SparkExamplesScript.scala

import org.apache.spark
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkConf

def asInt(s: String): Option[Int] = {
  try {
    Some(Integer.parseInt(s))
  } catch {
    case _ : Throwable => None
  }
}

// simulate "reading from a file" by converting a standard Scala list
// comprehension to an RDD
val fileData = for (i <- 1 to 1000000) yield {
  "%d\t%s".format(i, i)
}
val file: RDD[String] = sc.parallelize(fileData)
println("count: " + file.count())  // THIS WORKS AND PRINTS 1000000

// issue a `map()` on this RDD to convert string rows into integer tuples
// while skipping failures
val data: RDD[(Int, Int)] = file.flatMap(row => {
  val intRow = row.split('\t').map(asInt(_)).toList
  intRow match {
    case Some(key) :: Some(value) :: Nil =>
      Some((key, value))
    case _ =>
      None
  }
})

println("count: " + data.count())  // THIS WORKS AND PRINTS 1000000