如何从包含枚举的案例类创建 Spark 数据集或数据框 [英] How to create Spark Dataset or Dataframe from case classes that contains Enums

问题描述

我一直在尝试使用包含枚举的案例类创建 Spark 数据集，但我无法做到.我使用的是 Spark 1.6.0 版.例外是抱怨没有为我的 Enum 找到编码器.这在 Spark 中是不可能的，在数据​​中有枚举吗?

I have been trying to create Spark Dataset using case classes that contain Enums but I'm not able to. I'm using Spark version 1.6.0. The exceptions is complaining about that there are no encoder found for my Enum. Is this not possible in Spark, to have enums in the data?

代码:

import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}

object MyEnum extends Enumeration {
  type MyEnum = Value
  val Hello, World = Value
}

case class MyData(field: String, other: MyEnum.Value)

object EnumTest {

  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    val sqlCtx = new SQLContext(sc)

    import sqlCtx.implicits._

    val df = sc.parallelize(Array(MyData("hello", MyEnum.World))).toDS()

    println(s"df: ${df.collect().mkString(",")}}")
  }

}

错误:

Exception in thread "main" java.lang.UnsupportedOperationException: No Encoder found for com.company.MyEnum.Value
- field (class: "scala.Enumeration.Value", name: "other")
- root class: "com.company.MyData"
at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$extractorFor(ScalaReflection.scala:597)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$extractorFor$1.apply(ScalaReflection.scala:509)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$extractorFor$1.apply(ScalaReflection.scala:502)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.immutable.List.foreach(List.scala:318)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105)
at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$extractorFor(ScalaReflection.scala:502)
at org.apache.spark.sql.catalyst.ScalaReflection$.extractorsFor(ScalaReflection.scala:394)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:54)
at org.apache.spark.sql.SQLImplicits.newProductEncoder(SQLImplicits.scala:41)
at com.company.EnumTest$.main(EnumTest.scala:22)
at com.company.EnumTest.main(EnumTest.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)

推荐答案

您可以创建自己的编码器:

You can create your own encoder:

import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}

object MyEnum extends Enumeration {
  type MyEnum = Value
  val Hello, World = Value
}

case class MyData(field: String, other: MyEnum.Value)

object MyDataEncoders {
  implicit def myDataEncoder: org.apache.spark.sql.Encoder[MyData] =
    org.apache.spark.sql.Encoders.kryo[MyData]
}  

object EnumTest {
  import MyDataEncoders._

  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("test").setMaster("local[*]")
    val sc = new SparkContext(sparkConf)
    val sqlCtx = new SQLContext(sc)

    import sqlCtx.implicits._

    val df = sc.parallelize(Array(MyData("hello", MyEnum.World))).toDS()

    println(s"df: ${df.collect().mkString(",")}}")
  }
}

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