rdblue commented on a change in pull request #25024: [SPARK-27296][SQL] User
Defined Aggregators that do not ser/de on each input row
URL: https://github.com/apache/spark/pull/25024#discussion_r336668483
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
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@@ -450,3 +452,165 @@ case class ScalaUDAF(
override def nodeName: String = udaf.getClass.getSimpleName
}
+
+/**
+ * The internal wrapper used to hook a [[UserDefinedImperativeAggregator]]
`udia` in the
+ * internal aggregation code path.
+ */
+case class ScalaUDIA[T](
+ children: Seq[Expression],
+ udia: UserDefinedImperativeAggregator[T],
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0)
+ extends TypedImperativeAggregate[T]
+ with NonSQLExpression
+ with UserDefinedExpression
+ with ImplicitCastInputTypes
+ with Logging {
+
+ def dataType: DataType = udia.resultType
+
+ val inputTypes: Seq[DataType] = udia.inputSchema.map(_.dataType)
Review comment:
As you said, I think this is important for performance. It also affects the
output type. One valid way to handle null input values is to make the output
null. For example, `avg(1, null, 3)` is `null`. If input values are guaranteed
to be non-null, then functions like this would guarantee a non-null result and
wouldn't need to check for null values.
I think it's important that this be a full struct type, not just the
individual data types.
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