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Posted to issues@spark.apache.org by "Wenchen Fan (JIRA)" <ji...@apache.org> on 2016/06/23 03:07:16 UTC
[jira] [Resolved] (SPARK-15230) Back quoted column with dot in it
fails when running distinct on dataframe
[ https://issues.apache.org/jira/browse/SPARK-15230?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wenchen Fan resolved SPARK-15230.
---------------------------------
Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 13140
[https://github.com/apache/spark/pull/13140]
> Back quoted column with dot in it fails when running distinct on dataframe
> --------------------------------------------------------------------------
>
> Key: SPARK-15230
> URL: https://issues.apache.org/jira/browse/SPARK-15230
> Project: Spark
> Issue Type: Bug
> Components: Examples
> Affects Versions: 1.6.0
> Reporter: Barry Becker
> Fix For: 2.0.0
>
>
> When working with a dataframe columns with .'s in them must be backquoted (``) or the column name will not be found. This works for most dataframe methods, but I discovered that it does not work for distinct().
> Suppose you have a dataFrame, testDf, with a DoubleType column named {{pos.NoZero}}. This statememt:
> {noformat}
> testDf.select(new Column("`pos.NoZero`")).distinct().collect().mkString(", ")
> {noformat}
> will fail with this error:
> {noformat}
> org.apache.spark.sql.AnalysisException: Cannot resolve column name "pos.NoZero" among (pos.NoZero);
> at org.apache.spark.sql.DataFrame$$anonfun$resolve$1.apply(DataFrame.scala:152)
> at org.apache.spark.sql.DataFrame$$anonfun$resolve$1.apply(DataFrame.scala:152)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.sql.DataFrame.resolve(DataFrame.scala:151)
> at org.apache.spark.sql.DataFrame$$anonfun$dropDuplicates$1$$anonfun$40.apply(DataFrame.scala:1329)
> at org.apache.spark.sql.DataFrame$$anonfun$dropDuplicates$1$$anonfun$40.apply(DataFrame.scala:1329)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
> at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at org.apache.spark.sql.DataFrame$$anonfun$dropDuplicates$1.apply(DataFrame.scala:1329)
> at org.apache.spark.sql.DataFrame$$anonfun$dropDuplicates$1.apply(DataFrame.scala:1328)
> at org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$withPlan(DataFrame.scala:2165)
> at org.apache.spark.sql.DataFrame.dropDuplicates(DataFrame.scala:1328)
> at org.apache.spark.sql.DataFrame.dropDuplicates(DataFrame.scala:1348)
> at org.apache.spark.sql.DataFrame.dropDuplicates(DataFrame.scala:1319)
> at org.apache.spark.sql.DataFrame.distinct(DataFrame.scala:1612)
> at com.mineset.spark.vizagg.selection.SelectionExpressionSuite$$anonfun$40.apply$mcV$sp(SelectionExpressionSuite.scala:317)
> {noformat}
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