[jira] [Reopened] (SPARK-17024) Weird behaviour of the DataFrame when a column name contains dots.

["Iaroslav Zeigerman (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17024?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Iaroslav Zeigerman reopened SPARK-17024:
----------------------------------------

The issue occurs in Spark 2.0.0. Now it's even worse. I can't even get an rdd from a DataFrame. Backquotes doesn't help any more.

> Weird behaviour of the DataFrame when a column name contains dots.
> ------------------------------------------------------------------
>
>                 Key: SPARK-17024
>                 URL: https://issues.apache.org/jira/browse/SPARK-17024
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.6.0
>            Reporter: Iaroslav Zeigerman
>
> When a column name contains dots and one of the segment in a name is the same as other column's name, Spark treats this column as a nested structure, although the actual type of column is String/Int/etc. Example:
> {code}
>       val df = sqlContext.createDataFrame(Seq(
>         ("user1", "task1"),
>         ("user2", "task2")
>       )).toDF("user", "user.task")
> {code}
> Two columns "user" and "user.task". Both of them are string, and the schema resolution seems to be correct:
> {noformat}
> root
>  |-- user: string (nullable = true)
>  |-- user.task: string (nullable = true)
> {noformat}
> But when I'm trying to query this DataFrame like i.e.:
> {code}
>       df.select(df("user"), df("user.task"))
> {code}
> Spark throws an exception "Can't extract value from user#2;" 
> It happens during the resolution of the LogicalPlan while processing the  "user.task" column.
> Here is the full stacktrace:
> {noformat}
> Can't extract value from user#2;
> org.apache.spark.sql.AnalysisException: Can't extract value from user#2;
> 	at org.apache.spark.sql.catalyst.expressions.ExtractValue$.apply(complexTypeExtractors.scala:73)
> 	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$4.apply(LogicalPlan.scala:276)
> 	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$4.apply(LogicalPlan.scala:275)
> 	at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
> 	at scala.collection.immutable.List.foldLeft(List.scala:84)
> 	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:275)
> 	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveQuoted(LogicalPlan.scala:191)
> 	at org.apache.spark.sql.DataFrame.resolve(DataFrame.scala:151)
> 	at org.apache.spark.sql.DataFrame.col(DataFrame.scala:708)
> 	at org.apache.spark.sql.DataFrame.apply(DataFrame.scala:696)
> {noformat}
> Is this actually an expected behaviour? 



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