HyukjinKwon commented on a change in pull request #28645:
URL: https://github.com/apache/spark/pull/28645#discussion_r440098735
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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(_, _, inputs, encoders, _, _, _) if
encoders.nonEmpty =>
Review comment:
Shall we avoid argument matching? It's actually an anti-pattern -
https://github.com/databricks/scala-style-guide#pattern-matching
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