Task not serializable while using custom dataframe class in Spark Scala - scala

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.