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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2016/01/04 05:50:39 UTC
[jira] [Updated] (SPARK-12562) DataFrame.write.format("text")
requires the column name to be called value
[ https://issues.apache.org/jira/browse/SPARK-12562?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Reynold Xin updated SPARK-12562:
--------------------------------
Assignee: Xiu (Joe) Guo (was: Apache Spark)
> DataFrame.write.format("text") requires the column name to be called value
> --------------------------------------------------------------------------
>
> Key: SPARK-12562
> URL: https://issues.apache.org/jira/browse/SPARK-12562
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.0
> Reporter: Michael Armbrust
> Assignee: Xiu (Joe) Guo
> Fix For: 1.6.1, 2.0.0
>
>
> We should support writing any DataFrame that has a single string column, independent of the name.
> {code}
> wiki.select("text")
> .limit(10000)
> .write
> .format("text")
> .mode("overwrite")
> .save("/home/michael/wiki.txt")
> {code}
> {code}
> org.apache.spark.sql.AnalysisException: cannot resolve 'value' given input columns text;
> 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$$anonfun$transformUp$1.apply(TreeNode.scala:319)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:53)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:318)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:107)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:117)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:121)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:121)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:125)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:125)
> 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$1.apply(CheckAnalysis.scala:50)
> at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:105)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:50)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:44)
> at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)
> at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)
> at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:106)
> at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:58)
> at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:56)
> at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:70)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
> at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
> at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
> at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:256)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:148)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:139)
> {code}
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