Spark数据集和scala.ScalaReflectionException：类型V不是类

问题描述 投票：1回答：1

我有以下课程：

case class S1(value: String, ws: Map[Int, String])
case class S2(value: String, ws: Map[Int, String], dep: BS)

如上所示，这两个有一个不同的领域，即BS

下面的代码工作正常。

sparkSQL.createDataset(Seq(S1("heloo", Map(0 -> "0")))).foreach(x => println(x))

下面的代码也很好，它本身就是BS类。

sparkSQL.createDataset(Seq(BS(List(0), List(Edge(0, 1, DepRelation("0-->1", "", "")))))).foreach(x => println(x))

现在，如果我使用基本上是S2和S1类的BS，我会得到一个运行时错误消息：

sparkSQL.createDataset(Seq(S2("heloo", Map(0 -> "0"), BS(List(0), List(Edge(0, 1, DepRelation("0-->1", "", ""))))))).foreach(x => println(x))

Exception in thread "main" scala.ScalaReflectionException: type V is not a class
    at scala.reflect.api.Symbols$SymbolApi$class.asClass(Symbols.scala:275)
    at scala.reflect.internal.Symbols$SymbolContextApiImpl.asClass(Symbols.scala:84)
    at org.apache.spark.sql.catalyst.ScalaReflection$.getClassFromType(ScalaReflection.scala:689)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:84)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:66)
    at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
    at org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:809)
    at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
    at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor(ScalaReflection.scala:65)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.toCatalystArray$1(ScalaReflection.scala:458)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:503)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:455)
    at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
    at org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:809)
    at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
    at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:455)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1$$anonfun$10.apply(ScalaReflection.scala:626)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1$$anonfun$10.apply(ScalaReflection.scala:614)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.immutable.List.foreach(List.scala:392)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at scala.collection.immutable.List.flatMap(List.scala:355)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:614)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:455)
    at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
    at org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:809)
    at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
    at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:455)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1$$anonfun$10.apply(ScalaReflection.scala:626)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1$$anonfun$10.apply(ScalaReflection.scala:614)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.immutable.List.foreach(List.scala:392)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at scala.collection.immutable.List.flatMap(List.scala:355)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:614)
    at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:455)
    at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
    at org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:809)
    at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
    at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:455)
    at org.apache.spark.sql.catalyst.ScalaReflection$.serializerFor(ScalaReflection.scala:444)
    at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:71)
    at org.apache.spark.sql.Encoders$.product(Encoders.scala:275)
    at org.apache.spark.sql.LowPrioritySQLImplicits$class.newProductEncoder(SQLImplicits.scala:233)
    at org.apache.spark.sql.SQLImplicits.newProductEncoder(SQLImplicits.scala:33)

这是我的环境：

val conf = new SparkConf()
    .setAppName("test")
    .setMaster("local[*]")

val spark = new SparkContext(conf)
val sparkSQL = new SQLContext(spark)

import sparkSQL.implicits._

- 评论中每个请求的编辑1-- Edge和DepRel定义

case class Edge[V,E](from:V, to:V, label:E)
case class DepRelation(vl:String)
scala apache-spark apache-spark-sql apache-spark-dataset
1个回答
0
投票

从您的错误堆栈跟踪中可以明显看出，它无法满足以下要求。

Exception in thread "main" scala.ScalaReflectionException: type V is not a class

所以，这里的主要难点是Type V，您已经用它来定义以下case类。

case class Edge[V,E](from:V, to:V, label:E)

什么类型的V（和E？）。例如，它们是String / Int / Double吗？你能告诉我这里的代码吗？ （因为这里是你问题的答案）

如果您已经确定了那些V和E的类型信息，那么尝试重写Edge的case类定义，如下所示（通过推断实际类型）。对于例如

case class Edge[V: Int,E: Int](from:V, to:V, label:E)

它可能会解决您的问题，或者您可能需要进一步查看您的类型V.

希望这可以帮助。

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