Task not serializable while using custom dataframe

Question

I am facing a strange issue with Scala/Spark (1.5) and Zeppelin:

If I run the following Scala/Spark code, it will run properly:

// TEST NO PROBLEM SERIALIZATION
val rdd = sc.parallelize(Seq(1, 2, 3))
val testList = List[String]("a", "b")

rdd.map{a => 
    val aa = testList(0)
    None}

However after declaring a custom dataframe type as proposed here

//DATAFRAME EXTENSION
import org.apache.spark.sql.DataFrame

object ExtraDataFrameOperations {
  implicit class DFWithExtraOperations(df : DataFrame) {

    //drop several columns
    def drop(colToDrop:Seq[String]):DataFrame = {
        var df_temp = df
        colToDrop.foreach{ case (f: String) =>
            df_temp = df_temp.drop(f)//can be improved with Spark 2.0
        }
        df_temp
    }   
  }
}

and using it for example like following:

//READ ALL THE FILES INTO different DF and save into map
import ExtraDataFrameOperations._
val filename = "myInput.csv"

val delimiter =  ","

val colToIgnore = Seq("c_9", "c_10")

val inputICFfolder = "hdfs:///group/project/TestSpark/"

val df = sqlContext.read
            .format("com.databricks.spark.csv")
            .option("header", "true") // Use first line of all files as header
            .option("inferSchema", "false") // Automatically infer data types? => no cause we need to merge all df, with potential null values => keep string only
            .option("delimiter", delimiter)
            .option("charset", "UTF-8")
            .load(inputICFfolder + filename)
            .drop(colToIgnore)//call the customize dataframe

This run successfully.

Now if I run again the following code (same as above)

// TEST NO PROBLEM SERIALIZATION
val rdd = sc.parallelize(Seq(1, 2, 3))
val testList = List[String]("a", "b")
rdd.map{a => 
    val aa = testList(0)
    None}

I get the error message:

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[8] at parallelize at :32 testList: List[String] = List(a, b) org.apache.spark.SparkException: Task not serializable at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:304) at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:294) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122) at org.apache.spark.SparkContext.clean(SparkContext.scala:2032) at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:314) ... Caused by: java.io.NotSerializableException: $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$ExtraDataFrameOperations$ Serialization stack: - object not serializable (class: $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$ExtraDataFrameOperations$, value: $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$ExtraDataFrameOperations$@6c7e70e) - field (class: $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC, name: ExtraDataFrameOperations$module, type: class $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$ExtraDataFrameOperations$) - object (class $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC, $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC@4c6d0802) - field (class: $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC, name: $iw, type: class $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC) ...

I don't understand:

• Why this error occured while no operation on dataframe is performed?
• Why "ExtraDataFrameOperations" is not serializable while it was successfully used before??

UPDATE:

Trying with

@inline val testList = List[String]("a", "b")

does not help.

Answer 1

Just add 'extends Serializable' This work for me

/**
   * A wrapper around ProducerRecord RDD that allows to save RDD to Kafka.
   *
   * KafkaProducer is shared within all threads in one executor.
   * Error handling strategy - remember "last" seen exception and rethrow it to allow task fail.
   */
 implicit class DatasetKafkaSink(ds: Dataset[ProducerRecord[String, GenericRecord]]) extends Serializable {

   class ExceptionRegisteringCallback extends Callback {
     private[this] val lastRegisteredException = new AtomicReference[Option[Exception]](None)

     override def onCompletion(metadata: RecordMetadata, exception: Exception): Unit = {
       Option(exception) match {
         case a @ Some(_) => lastRegisteredException.set(a) // (re)-register exception if send failed
         case _ => // do nothing if encountered successful send
       }
     }

     def rethrowException(): Unit = lastRegisteredException.getAndSet(None).foreach(e => throw e)
   }

   /**
     * Save to Kafka reusing KafkaProducer from singleton holder.
     * Returns back control only once all records were actually sent to Kafka, in case of error rethrows "last" seen
     * exception in the same thread to allow Spark task to fail
     */
   def saveToKafka(kafkaProducerConfigs: Map[String, AnyRef]): Unit = {
     ds.foreachPartition { records =>
       val callback = new ExceptionRegisteringCallback
       val producer = KafkaProducerHolder.getInstance(kafkaProducerConfigs)

       records.foreach(record => producer.send(record, callback))

       producer.flush()
       callback.rethrowException()
     }
   }
 }'

Answer 2

It looks like spark tries to serialize all the scope around testList. Try to inline data @inline val testList = List[String]("a", "b") or use different object where you store function/data which you pass to drivers.