Liang-Chi Hsieh created SPARK-19425:
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Summary: 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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