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[SPARK-31826][SQL] Support composed type of case class for typed Scala UDF #28645

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[SPARK-31826][SQL] Support composed type of case class for typed Scala UDF #28645

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Ngone51 May 12, 2020
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remove unused import
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check UDT recursively
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address pattern
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also for Java UDF
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33 changes: 32 additions & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -256,7 +256,8 @@ class Analyzer(
Batch("Nondeterministic", Once,
PullOutNondeterministic),
Batch("UDF", Once,
HandleNullInputsForUDF),
HandleNullInputsForUDF,
ResolveEncodersInUDF),
Batch("UpdateNullability", Once,
UpdateAttributeNullability),
Batch("Subquery", Once,
Expand Down Expand Up @@ -2847,6 +2848,36 @@ class Analyzer(
}
}

object ResolveEncodersInUDF extends Rule[LogicalPlan] {
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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 =>

@HyukjinKwon HyukjinKwon Jun 15, 2020

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Shall we avoid argument matching? It's actually an anti-pattern - https://github.com/databricks/scala-style-guide#pattern-matching

@Ngone51 Ngone51 Jun 18, 2020

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I see, thanks!

val resolvedEncoders = encoders.zipWithIndex.map { case (encOpt, i) =>

@cloud-fan cloud-fan Jun 9, 2020

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maybe to call it boundEncoders.

val dataType = inputs(i).dataType
if (dataType.isInstanceOf[UserDefinedType[_]]) {

@cloud-fan cloud-fan Jun 9, 2020

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what about struct/array/map of UDT?

// for UDT, we use `CatalystTypeConverters`

@cloud-fan cloud-fan Jun 18, 2020

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encoder does support UDT. We can figure it out later.

@Ngone51 Ngone51 Jun 18, 2020

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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.

None
} else {
encOpt.map { enc =>
val attrs = if (enc.isSerializedAsStructForTopLevel) {
dataType.asInstanceOf[StructType].toAttributes
} else {
// the field name doesn't matter here, so we use
// a simple literal to avoid any overhead
new StructType().add("input", dataType).toAttributes
}
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enc.resolveAndBind(attrs)
}
}
}
udf.copy(inputEncoders = resolvedEncoders)
}
}
}

/**
* Check and add proper window frames for all window functions.
*/
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