[jira] [Comment Edited] (SPARK-14048) Aggregation operations on structs fail when the structs have fields with special characters

["Simeon Simeonov (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-14048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15210213#comment-15210213 ] 

Simeon Simeonov edited comment on SPARK-14048 at 3/24/16 1:31 PM:
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You can see the problem on a 1.6.0 cluster in the Databricks environment in the attached file ({{bug_structs_with_backticks.html}}). Perhaps the issue is not as simple as I assumed it to be and may require more complex types?


was (Author: simeons):
You can see the problem on a 1.6.0 cluster in the Databricks environment in the attached file. Perhaps the issue is not as simple as I assumed it to be and may require more complex types?

> Aggregation operations on structs fail when the structs have fields with special characters
> -------------------------------------------------------------------------------------------
>
>                 Key: SPARK-14048
>                 URL: https://issues.apache.org/jira/browse/SPARK-14048
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.6.0
>         Environment: Databricks w/ 1.6.0
>            Reporter: Simeon Simeonov
>              Labels: sql
>         Attachments: bug_structs_with_backticks.html
>
>
> Consider a schema where a struct has field names with special characters, e.g.,
> {code}
>  |-- st: struct (nullable = true)
>  |    |-- x.y: long (nullable = true)
> {code}
> Schema such as these are frequently generated by the JSON schema generator, which seems to never want to map JSON data to {{MapType}} always preferring to use {{StructType}}. 
> In SparkSQL, referring to these fields requires backticks, e.g., {{st.`x.y`}}. There is no problem manipulating these structs unless one is using an aggregation function. It seems that, under the covers, the code is not escaping fields with special characters correctly.
> For example, 
> {code}
> select first(st) as st from tbl group by something
> {code}
> generates
> {code}
> org.apache.spark.sql.catalyst.util.DataTypeException: Unsupported dataType: struct<x.y:bigint>. If you have a struct and a field name of it has any special characters, please use backticks (`) to quote that field name, e.g. `x+y`. Please note that backtick itself is not supported in a field name.
>   at org.apache.spark.sql.catalyst.util.DataTypeParser$class.toDataType(DataTypeParser.scala:100)
>   at org.apache.spark.sql.catalyst.util.DataTypeParser$$anon$1.toDataType(DataTypeParser.scala:112)
>   at org.apache.spark.sql.catalyst.util.DataTypeParser$.parse(DataTypeParser.scala:116)
>   at org.apache.spark.sql.hive.HiveMetastoreTypes$.toDataType(HiveMetastoreCatalog.scala:884)
>   at com.databricks.backend.daemon.driver.OutputAggregator$$anonfun$toJsonSchema$1.apply(OutputAggregator.scala:395)
>   at com.databricks.backend.daemon.driver.OutputAggregator$$anonfun$toJsonSchema$1.apply(OutputAggregator.scala:394)
>   at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>   at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>   at scala.collection.immutable.List.foreach(List.scala:318)
>   at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>   at scala.collection.AbstractTraversable.map(Traversable.scala:105)
>   at com.databricks.backend.daemon.driver.OutputAggregator$.toJsonSchema(OutputAggregator.scala:394)
>   at com.databricks.backend.daemon.driver.OutputAggregator$.maybeApplyOutputAggregation(OutputAggregator.scala:122)
>   at com.databricks.backend.daemon.driver.OutputAggregator$.withOutputAggregation0(OutputAggregator.scala:82)
>   at com.databricks.backend.daemon.driver.OutputAggregator$.withOutputAggregation(OutputAggregator.scala:42)
>   at com.databricks.backend.daemon.driver.DriverLocal.executeSql(DriverLocal.scala:306)
>   at com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:161)
>   at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$3.apply(DriverWrapper.scala:467)
>   at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$3.apply(DriverWrapper.scala:467)
>   at scala.util.Try$.apply(Try.scala:161)
>   at com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:464)
>   at com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:365)
>   at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:196)
>   at java.lang.Thread.run(Thread.java:745)
> {code}



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