upgrade from spark 1.2.1 to 1.3 on EC2 cluster and problems

[roni]

I have a EC2 cluster created using spark version 1.2.1.
And I have a SBT project .
Now I want to upgrade to spark 1.3 and use the new features.
Below are issues .
Sorry for the long post.
Appreciate your help.
Thanks
-Roni

Question - Do I have to create a new cluster using spark 1.3?

Here is what I did -

In my SBT file I  changed to -
libraryDependencies += "org.apache.spark" %% "spark-core" % "1.3.0"

But then I started getting compilation error. along with
Here are some of the libraries that were evicted:
[warn]  * org.apache.spark:spark-core_2.10:1.2.0 -> 1.3.0
[warn]  * org.apache.hadoop:hadoop-client:(2.5.0-cdh5.2.0, 2.2.0) -> 2.6.0
[warn] Run 'evicted' to see detailed eviction warnings

 constructor cannot be instantiated to expected type;
[error]  found   : (T1, T2)
[error]  required: org.apache.spark.sql.catalyst.expressions.Row
[error]                                 val ty = joinRDD.map{case(word,
(file1Counts, file2Counts)) => KmerIntesect(word, file1Counts,"xyz")}
[error]                                                          ^

Here is my SBT and code --
SBT -

version := "1.0"

scalaVersion := "2.10.4"

resolvers += "Sonatype OSS Snapshots" at "
https://oss.sonatype.org/content/repositories/snapshots";;
resolvers += "Maven Repo1" at "https://repo1.maven.org/maven2";;
resolvers += "Maven Repo" at "
https://s3.amazonaws.com/h2o-release/h2o-dev/master/1056/maven/repo/";;

/* Dependencies - %% appends Scala version to artifactId */
libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "2.6.0"
libraryDependencies += "org.apache.spark" %% "spark-core" % "1.3.0"
libraryDependencies += "org.bdgenomics.adam" % "adam-core" % "0.16.0"
libraryDependencies += "ai.h2o" % "sparkling-water-core_2.10" % "0.2.10"


CODE --
import org.apache.spark.{SparkConf, SparkContext}
case class KmerIntesect(kmer: String, kCount: Int, fileName: String)

object preDefKmerIntersection {
  def main(args: Array[String]) {

 val sparkConf = new SparkConf().setAppName("preDefKmer-intersect")
     val sc = new SparkContext(sparkConf)
        import sqlContext.createSchemaRDD
        val sqlContext = new org.apache.spark.sql.SQLContext(sc)
            val bedFile = sc.textFile("s3n://a/b/c",40)
             val hgfasta = sc.textFile("hdfs://a/b/c",40)
                 val hgPair = hgfasta.map(_.split (",")).map(a=> (a(0),
a(1).trim().toInt))
                 val filtered = hgPair.filter(kv => kv._2 == 1)
                 val bedPair = bedFile.map(_.split (",")).map(a=> (a(0),
a(1).trim().toInt))
                 val joinRDD = bedPair.join(filtered)
                val ty = joinRDD.map{case(word, (file1Counts, file2Counts))
=> KmerIntesect(word, file1Counts,"xyz")}
                ty.registerTempTable("KmerIntesect")
                ty.saveAsParquetFile("hdfs://x/y/z/kmerIntersect.parquet")
  }
}