Mocking HTable data for a unit test of a spark job

I have a Scala spark job that reads from HBase like so:

val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], classOf[org.apache.hadoop.hbase.client.Result])
val uniqueAttrs = calculateFreqLocation(hBaseRDD)

I am trying to write a unit test for the function calculateFreqLocation:

 def calculateFreqLocation(inputRDD: RDD[(ImmutableBytesWritable, Result)]): Map[String, Map[(String, String, String), Long]] =  {
    val valueType = classOf[Array[Attribute]]
    val family = "cf_attributes".getBytes()
    val qualifier = "attributes".getBytes()
    val rdd7 = inputRDD.map(kv => (getUUID(kv._1.get()).toString(),
      objectMapper.readValue(new String(kv._2.getValue(family, qualifier)), valueType))).flatMap(flattenRow).filter(t => location_attributes.contains(t._2))

    val countByUUID = rdd7.countByValue().groupBy(_._1._1)
    val countByUUIDandKey = countByUUID.map(kv => (kv._1, kv._2.groupBy(_._1._2)))
    val uniqueAttrs = countByUUIDandKey.map(uuidmap => (uuidmap._1,uuidmap._2.map(keymap => keymap._2.maxBy(_._2))))
    return uniqueAttrs
  }

This counts unique attributes for each UUID. My unit test tries to recreate the HTable data and then pass the RDD to the function to see if the output matches:

@RunWith(classOf[JUnitRunner])
class FrequentLocationTest extends SparkJobSpec {
    "Frequent Location calculation" should {

    def longToBytes(x: Long): Array[Byte] = {
      return ByteBuffer.allocate(java.lang.Long.SIZE / java.lang.Byte.SIZE).putLong(x).array
    }
    val currTimestamp = System.currentTimeMillis / 1000
    val UUID_1 = UUID.fromString("123456aa-8f07-4190-8c40-c7e78b91a646")
    val family = "cf_attributes".getBytes()
    val column = "attributes".getBytes()
    val row = "[{'name':'Current_Location_Ip_Address', 'value':'123.456.123.248'}]"

    val resultRow = Array(new KeyValue(row.getBytes(), family, column, null))

    val key = "851971aa-8f07-4190-8c40-c7e78b91a646".getBytes() ++ longToBytes(currTimestamp)
    val input = Seq((key,row))
    val correctOutput = Map(
      ("851971aa-8f07-4190-8c40-c7e78b91a646" -> Map(("123456aa-8f07-4190-8c40-c7e78b91a646","Current_Location_Ip_Address","123.456.123.248") -> 1))
      )

    "case 1 : return with correct output (frequent location calculation)" in {
      val inputRDD = sc.makeRDD(input, 1)
      val hadoonRdd = new HadoopRDD(sc, sc.broadcast(new SerializableWritable(new Configuration()))
        .asInstanceOf[Broadcast[SerializableWritable[Configuration]]], null, classOf[InputFormat[ImmutableBytesWritable,Result]], classOf[ImmutableBytesWritable],classOf[Result],1)

      val finalInputRdd = hadoonRdd.union(inputRDD.map(kv => ( new ImmutableBytesWritable(kv._1), new Result(Array(new KeyValue(kv._2.getBytes(), family, column, null))))))

      val resultMap = FrequentLocation.calculateFreqLocation(finalInputRdd)
      resultMap == correctOutput
      //val customCorr = new FrequentLocation().calculateFreqLocation(inputRDD)
      //freqLocationMap must_== correctOutput
    }
  }
}

What I get is org.apache.spark.SparkException: Task not serializable. I've come to understand it is because of the LongByteWritable and other HTable classes that spark cant serialize between nodes. The code I provided I am actually getting into developer Spark apis (creating the HadoopRDD manually) but dont have any way to actually populate this with data. How can I test this? I need to return an instance of a HadoopRDD with data in it to this function. Or an instance of RDD(ImmutableBytesWritable, Result). I was initially creating this RDD manually, same error. then I switched to using map and mapping it from raw binary/text. Any help would be appreciated!