GitHub user viirya opened a pull request:
https://github.com/apache/spark/pull/16765
[SPARK-19425][SQL] Make df.except work for UDT
## What changes were proposed in this pull request?
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.
## How was this patch tested?
Jenkins tests.
Please review http://spark.apache.org/contributing.html before opening a
pull request.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/viirya/spark-1 df-except-for-udt
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/16765.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #16765
----
commit a6252205545f0c5d907d26d1513fd0b0a4af8050
Author: Liang-Chi Hsieh <vii...@gmail.com>
Date: 2017-02-01T14:59:43Z
Make df.except work for UDT.
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