Liang-Chi Hsieh 0674e7eb85 [SPARK-19425][SQL] Make ExtractEquiJoinKeys support UDT columns ... ## 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.VectorUDT3bfc3ba7 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) More simple fix is just let `Literal.default` handle UDT by its sql type. So we can use more efficient join type on UDT. Besides `except`, this also fixes other similar scenarios, so in summary this fixes: * `except` on two Datasets with UDT * `intersect` on two Datasets with UDT * `Join` with the join conditions using `<=>` on UDT columns ## How was this patch tested? Jenkins tests. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #16765 from viirya/df-except-for-udt.		2017-02-04 15:57:56 -08:00
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main	[SPARK-19446][SQL] Remove unused findTightestCommonType in TypeCoercion	2017-02-03 22:10:17 -08:00
test	[SPARK-19425][SQL] Make ExtractEquiJoinKeys support UDT columns	2017-02-04 15:57:56 -08:00