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https://issues.apache.org/jira/browse/SPARK-19425?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Liang-Chi Hsieh updated SPARK-19425:
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Summary: Make ExtractEquiJoinKeys support UDT columns (was: Make df.except
work for UDT)
> Make ExtractEquiJoinKeys support UDT columns
> --------------------------------------------
>
> 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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