在Spark Scala中使用自定义数据框类时无法序列化任务

Question

我面对Scala / Spark（1.5）和Zeppelin的一个奇怪问题：

如果我运行以下Scala / Spark代码，它将正常运行：

// 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}

但是，在声明了自定义数据框类型后， 此处提出了建议

//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
    }   
  }
}

并像下面这样使用它：

//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

这样运行成功。

现在，如果我再次运行以下代码（与上面相同）

// 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}

我收到错误消息：

rdd：org.apache.spark.rdd.RDD [Int] = ParallelCollectionRDD [8]并行于：32 testList：List [String] = List（a，b）org.apache.spark.SparkException：任务无法在org上序列化org.apache.spark.util.ClosureCleaner $ .org $ apache $ spark $ util $ ClosureCleaner $$ clean（ClosureCleaner.scala：294）的.apache.spark.util.ClosureCleaner $ .ensureSerializable（ClosureCleaner.scala：304） org.apache.spark.SparkContext.clean（SparkContext.scala：2032）上的org.apache.spark.util.ClosureCleaner $ .clean（ClosureCleaner.scala：122）在org.apache.spark.rdd.RDD $ RDon $ RD map $ 1.apply（RDD.scala：314）...造成原因：java.io.NotSerializableException：$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $ ExtraDataFrameOperations $序列化堆栈：-无法序列化的对象（类：$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $ ExtraDataFrameOperations $，值：$ iwC $$ iwC $ $ iwC $$ iwC $$ iwC $$ iwC $ iwC $$ iwC $$ iwC $ ExtraDataFrameOperations $ @ 6c7e70e）-字段（类别：$ iwC $$ iwC $$ iwC $$ iwC $ iwC $$ iwC $$ iwC $$ iwC $$ iwC，名称：ExtraDataFrameOperations $ 模块，类型：类$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $ ExtraDataFrameOperations $）-对象（类$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $ iwC $$ iwC，$ iwC $$ iwC $ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC @ 4c6d0802）-字段（class： $ iwC $$ iwC $$ iwC $$ iwC $ iwC $$ iwC $$ iwC $$ iwC，名称：$ iw，类型：class $ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $ $ iwC $$ iwC $$ iwC）...

我不明白：

• 为什么在对数据帧不执行任何操作时发生此错误？
• 为什么“ ExtraDataFrameOperations”在以前成功使用后无法序列化？

更新：

尝试

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

没有帮助。

Answer 1

看起来spark尝试序列化testList周围的所有范围。 尝试内联数据@inline val testList = List[String]("a", "b")或使用其他对象来存储传递给驱动程序的函数/数据。

Answer 2

只需添加“ extends Serializable”即可为我工作

/**
   * 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()
     }
   }
 }'