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  1. Spark
  2. SPARK-13326

Dataset in spark 2.0.0-SNAPSHOT missing columns

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    Details

    • Type: Bug
    • Status: Resolved
    • Priority: Minor
    • Resolution: Later
    • Affects Version/s: 2.0.0
    • Fix Version/s: None
    • Component/s: SQL
    • Labels:
      None

      Description

      i noticed some things stopped working on datasets in spark 2.0.0-SNAPSHOT, and with a confusing error message (cannot resolved some column with input columns []).

      for example in 1.6.0-SNAPSHOT:

      scala> val ds = sc.parallelize(1 to 10).toDS
      ds: org.apache.spark.sql.Dataset[Int] = [value: int]
      
      scala> ds.map(x => Option(x))
      res0: org.apache.spark.sql.Dataset[Option[Int]] = [value: int]
      

      and same commands in 2.0.0-SNAPSHOT:

      scala> val ds = sc.parallelize(1 to 10).toDS
      ds: org.apache.spark.sql.Dataset[Int] = [value: int]
      
      scala> ds.map(x => Option(x))
      org.apache.spark.sql.AnalysisException: cannot resolve 'value' given input columns: [];
        at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
        at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:60)
        at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:284)
        at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:283)
        at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:162)
        at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:172)
        at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:176)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
        at scala.collection.immutable.List.foreach(List.scala:381)
        at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
        at scala.collection.immutable.List.map(List.scala:285)
        at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:176)
        at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:181)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:370)
        at scala.collection.Iterator$class.foreach(Iterator.scala:742)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1194)
        at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
        at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:308)
        at scala.collection.AbstractIterator.to(Iterator.scala:1194)
        at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:300)
        at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1194)
        at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:287)
        at scala.collection.AbstractIterator.toArray(Iterator.scala:1194)
        at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:181)
        at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:57)
        at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:50)
        at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:122)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:121)
        at scala.collection.immutable.List.foreach(List.scala:381)
        at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:121)
        at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50)
        at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:46)
        at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.resolve(ExpressionEncoder.scala:322)
        at org.apache.spark.sql.Dataset.<init>(Dataset.scala:81)
        at org.apache.spark.sql.Dataset.<init>(Dataset.scala:92)
        at org.apache.spark.sql.Dataset.mapPartitions(Dataset.scala:339)
        at org.apache.spark.sql.Dataset.map(Dataset.scala:323)
        ... 43 elided
      

      i observed similar issues with user defined types (org.apache.spark.sql.types.UserDefinedType) in Dataset. trying to insert a UserDefinedType in Dataset[Row] fails with input columns [].

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              • Assignee:
                Unassigned
                Reporter:
                koert koert kuipers
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                • Created:
                  Updated:
                  Resolved: