如何从包含枚举的案例类中创建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来创建Spark数据集，但我无法这样做.我正在使用Spark版本1.6.0.异常抱怨我的枚举找不到编码器.在Spark中是否不可能在数据中包含枚举?

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(",")}}")
  }
}

这篇关于如何从包含枚举的案例类中创建Spark数据集或数据框的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！