Looks like a bug. I'm also not sure whether we support Option yet. (If not, we should definitely support that in 2.0.)
Can you file a JIRA ticket? On Mon, Feb 15, 2016 at 7:12 AM, Koert Kuipers <ko...@tresata.com> wrote: > 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 []. >
