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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2016/04/05 01:59:25 UTC
[jira] [Resolved] (SPARK-13326) Dataset in spark 2.0.0-SNAPSHOT
missing columns
[ https://issues.apache.org/jira/browse/SPARK-13326?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Reynold Xin resolved SPARK-13326.
---------------------------------
Resolution: Later
See SPARK-14155.
We will re-introduce UDT in Spark 2.1 to make it work with Datasets. This will require some design..
> Dataset in spark 2.0.0-SNAPSHOT missing columns
> -----------------------------------------------
>
> Key: SPARK-13326
> URL: https://issues.apache.org/jira/browse/SPARK-13326
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: koert kuipers
> Priority: Minor
>
> 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:
> {noformat}
> 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]
> {noformat}
> and same commands in 2.0.0-SNAPSHOT:
> {noformat}
> 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
> {noformat}
> 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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