Ngone51 commented on a change in pull request #28645:
URL: https://github.com/apache/spark/pull/28645#discussion_r442183888
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
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@@ -2847,6 +2848,45 @@ class Analyzer(
}
}
+ /**
+ * Resolve the encoders for the UDF by explicitly given the attributes. We
give the
+ * attributes explicitly in order to handle the case where the data type of
the input
+ * value is not the same with the internal schema of the encoder, which
could cause
+ * data loss. For example, the encoder should not cast the input value to
Decimal(38, 18)
+ * if the actual data type is Decimal(30, 0).
+ *
+ * The resolved encoders then will be used to deserialize the internal row
to Scala value.
+ */
+ object ResolveEncodersInUDF extends Rule[LogicalPlan] {
+ override def apply(plan: LogicalPlan): LogicalPlan =
plan.resolveOperatorsUp {
+ case p if !p.resolved => p // Skip unresolved nodes.
+
+ case p => p transformExpressionsUp {
+
+ case udf: ScalaUDF if udf.inputEncoders.nonEmpty =>
+ val boundEncoders = udf.inputEncoders.zipWithIndex.map { case
(encOpt, i) =>
+ val dataType = udf.children(i).dataType
+ if
(dataType.existsRecursively(_.isInstanceOf[UserDefinedType[_]])) {
+ // for UDT, we use `CatalystTypeConverters`
Review comment:
It does, but just doesn't support upcast from the subclass to the parent
class. So, when the input data type from the child is the subclass of the input
parameter data type of the udf, `resolveAndBind` can fail.
I think this may need a separate fix.
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