Spark streaming ready

I am learning Spark and trying to create a simple streaming service.

For example, I have a Kafka queue and a Spark job like word count . This example uses stateless mode. I would like to accumulate the word count, so if test

it was sent multiple times in different messages, I could get the total of all its occurrences.

Using other examples like StatefulNetworkWordCount I tried to change my Kafka streaming service

val sc = new SparkContext(sparkConf)
val ssc = new StreamingContext(sc, Seconds(2))

ssc.checkpoint("/tmp/data")

// Create direct kafka stream with brokers and topics
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topicsSet)

// Get the lines, split them into words, count the words and print
val lines = messages.map(_._2)
val words = lines.flatMap(_.split(" "))

val wordDstream = words.map(x => (x, 1))

// Update the cumulative count using mapWithState
// This will give a DStream made of state (which is the cumulative count of the words)
val mappingFunc = (word: String, one: Option[Int], state: State[Int]) => {
  val sum = one.getOrElse(0) + state.getOption.getOrElse(0)
  val output = (word, sum)
  state.update(sum)
  output
}

val stateDstream = wordDstream.mapWithState(
  StateSpec.function(mappingFunc) /*.initialState(initialRDD)*/)

stateDstream.print()

stateDstream.map(s => (s._1, s._2.toString)).foreachRDD(rdd => sc.toRedisZSET(rdd, "word_count", 0))

// Start the computation
ssc.start()
ssc.awaitTermination()

      

        
        
        
      

    

I am getting many errors like

17/03/26 21:33:57 ERROR streaming.StreamingContext: Error starting the context, marking it as stopped
java.io.NotSerializableException: DStream checkpointing has been enabled but the DStreams with their functions are not serializable
org.apache.spark.SparkContext
Serialization stack:
    - object not serializable (class: org.apache.spark.SparkContext, value: org.apache.spark.SparkContext@2b680207)
    - field (class: com.DirectKafkaWordCount$$anonfun$main$2, name: sc$1, type: class org.apache.spark.SparkContext)
    - object (class com.DirectKafkaWordCount$$anonfun$main$2, <function1>)
    - field (class: org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3, name: cleanedF$1, type: interface scala.Function1)

      

        
        
        
      

    

although the stateless version doesn't work without errors

val sc = new SparkContext(sparkConf)
val ssc = new StreamingContext(sc, Seconds(2))

// Create direct kafka stream with brokers and topics
val topicsSet = topics.split(",").toSet
val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
val messages = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
  ssc, kafkaParams, topicsSet)

// Get the lines, split them into words, count the words and print
val lines = messages.map(_._2)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _).map(s => (s._1, s._2.toString))
wordCounts.print()

wordCounts.foreachRDD(rdd => sc.toRedisZSET(rdd, "word_count", 0))

// Start the computation
ssc.start()
ssc.awaitTermination()

      

        
        
        
      

    

The question is how to do stateful word count streaming.