TaskSchedulerImpl: Initial job has not accepted any resources. (Error in Spark) - scala

Question

I'm trying to run SparkPi example on my standalone mode cluster.
package org.apache.spark.examples
import scala.math.random
import org.apache.spark._
/** Computes an approximation to pi */
object SparkPi {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("SparkPi")
.setMaster("spark://192.168.17.129:7077")
.set("spark.driver.allowMultipleContexts", "true")
val spark = new SparkContext(conf)
val slices = if (args.length > 0) args(0).toInt else 2
val n = math.min(100000L * slices, Int.MaxValue).toInt // avoid overflow
val count = spark.parallelize(1 until n, slices).map { i =>
val x = random * 2 - 1
val y = random * 2 - 1
if (x*x + y*y < 1) 1 else 0
}.reduce(_ + _)
println("Pi is roughly " + 4.0 * count / n)
spark.stop()
}
}
Note: I made a little change in this line:
val conf = new SparkConf().setAppName("SparkPi")
.setMaster("spark://192.168.17.129:7077")
.set("spark.driver.allowMultipleContexts", "true")
Problem: I'm using spark-shell (Scala interface) to run this code. When I try this code, I receive this error repeatedly:
15/02/09 06:39:23 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
Note: I can see my workers in my Master's WebUI and also I can see a new job in the Running Applications section. But there is no end for this application and I see error repeatedly.
What is the problem?
Thanks

Answer 1

If you want to run this from spark shell, then start the shell with argument --master spark://192.168.17.129:7077 and enter the following code:
import scala.math.random
import org.apache.spark._
val slices = 10
val n = math.min(100000L * slices, Int.MaxValue).toInt // avoid overflow
val count = sc.parallelize(1 until n, slices).map { i =>
val x = random * 2 - 1
val y = random * 2 - 1
if (x*x + y*y < 1) 1 else 0
}.reduce(_ + _)
println("Pi is roughly " + 4.0 * count / n)
Otherwise, compile the code into a jar and run it with spark-submit. But remove setMaster from the code and add it as 'master' argument to spark-submit script. Also remove the allowMultipleContexts argument from the code.
You need only one spark context.