[jira] [Commented] (SPARK-19425) Make df.except work for UDT
[ https://issues.apache.org/jira/browse/SPARK-19425?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15848496#comment-15848496 ] Apache Spark commented on SPARK-19425: -- User 'viirya' has created a pull request for this issue: https://github.com/apache/spark/pull/16765 > 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$4.apply(patterns.scala:110) > We should simply skip using the columns whose types don't provide default > literal as joining key. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-19425) Make df.except work for UDT
[ https://issues.apache.org/jira/browse/SPARK-19425?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15848483#comment-15848483 ] Liang-Chi Hsieh commented on SPARK-19425: - I remember affects version can be None before. But when create this issue, it becomes required field. > Make df.except work for UDT > --- > > Key: SPARK-19425 > URL: https://issues.apache.org/jira/browse/SPARK-19425 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.1.0 >Reporter: Liang-Chi Hsieh > > DataFrame.except doesn't work for UDT columns. It is because > ExtractEquiJoinKeys will run Literal.default against UDT. However, we don't > handle UDT in Literal.default and an exception will throw like: > java.lang.RuntimeException: no default for type > org.apache.spark.ml.linalg.VectorUDT@3bfc3ba7 > at > org.apache.spark.sql.catalyst.expressions.Literal$.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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
