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From "Liang-Chi Hsieh (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-19425) Make df.except work for UDT
Date Wed, 01 Feb 2017 15:16:51 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19425?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15848483#comment-15848483
] 

Liang-Chi Hsieh commented on SPARK-19425:
-----------------------------------------

I remember affects version can be None before. But when create this issue, it becomes required
field.

> Make df.except work for UDT
> ---------------------------
>
>                 Key: SPARK-19425
>                 URL: https://issues.apache.org/jira/browse/SPARK-19425
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.1.0
>            Reporter: Liang-Chi Hsieh
>
> DataFrame.except doesn't work for UDT columns. It is because ExtractEquiJoinKeys will
run Literal.default against UDT. However, we don't handle UDT in Literal.default and an exception
will throw like:
> java.lang.RuntimeException: no default for type 
> org.apache.spark.ml.linalg.VectorUDT@3bfc3ba7
>   at org.apache.spark.sql.catalyst.expressions.Literal$.default(literals.scala:179)
>   at org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys$$anonfun$4.apply(patterns.scala:117)
>   at org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys$$anonfun$4.apply(patterns.scala:110)
> We should simply skip using the columns whose types don't provide default literal as
joining key.



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