spark streaming wordcount is not printing the results - scala

Question

I am trying to run this app in spark streaming the code is from a book I am reading but unfortunately I am not getting the expected results. There is a java class in which I open a socket and wait for an input. I run the socket code and connect it properly with the spark job. Then I submit the following job and I get a message that I connected successfully. When I type something in the socket I want to get a wordcount result printed in the terminal instead I am getting this message:
INFO BlockManagerInfo: Added input-0-1480077969600 in memory on 192.168.1.4:38818 (size: 7.0 B, free: 265.1 MB)
where is the problem? See the code bellow, thanks in advance
import org.apache.spark.SparkConf
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming._
import org.apache.spark.storage.StorageLevel._
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.dstream.ForEachDStream
object ScalaFirstStreamingExample {
def main(args: Array[String]){
println("Creating Spark Configuration") //Create an Object of Spark Configuration
val conf = new SparkConf() //Set the logical and user defined Name of this Application
conf.setAppName("My First Spark Streaming Application")
println("Retreiving Streaming Context from Spark Conf") //Retrieving Streaming Context from SparkConf Object.
//Second parameter is the time interval at which
//streaming data will be divided into batches
val streamCtx = new StreamingContext(conf, Seconds(2)) //Define the type of Stream. Here we are using TCP
//Socket as textstream,
//It will keep watching for the incoming data from a
//specific machine (localhost) and port (9087)
//Once the data is retrieved it will be saved in the
//memory and in case memory
//is not sufficient, then it will store it on the Disk
//It will further read the Data and convert it into DStream
val lines = streamCtx.socketTextStream("localhost", 9087, MEMORY_AND_DISK_SER_2) //Apply the Split() function to all elements of DStream
//which will further generate multiple new records from
//each record in Source Stream
//And then use flatmap to consolidate all records and
//create a new DStream.
val words = lines.flatMap(x => x.split(" ")) //Now, we will count these words by applying a using map()
//map() helps in applying a given function to each
//element in an RDD.
val pairs = words.map(word => (word, 1)) //Further we will aggregate the value of each key by
//using/applying the given function.
val wordCounts = pairs.reduceByKey(_ + _) //Lastly we will print all Values
//wordCounts.print(20)
myPrint(wordCounts,streamCtx)
//Most important statement which will initiate the
//Streaming Context
streamCtx.start();
//Wait till the execution is completed.
streamCtx.awaitTermination();
}
def myPrint(stream:DStream[(String,Int)],streamCtx: StreamingContext){
stream.foreachRDD(foreachFunc)
def foreachFunc = (rdd: RDD[(String,Int)]) => {
val array = rdd.collect()
println("---------Start Printing Results----------")
for(res<-array){
println(res)
}
println("---------Finished Printing Results----------")
}
}
}