Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/15703#discussion_r88171373
--- Diff: sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
---
@@ -289,73 +302,75 @@ private[hive] case class HiveUDAFFunction(
funcWrapper.createFunction[AbstractGenericUDAFResolver]()
}
+ // Hive `ObjectInspector`s for all child expressions (input parameters
of the function).
@transient
- private lazy val inspectors = children.map(toInspector).toArray
+ private lazy val inputInspectors = children.map(toInspector).toArray
+ // Spark SQL data types of input parameters.
@transient
- private lazy val functionAndInspector = {
- val parameterInfo = new SimpleGenericUDAFParameterInfo(inspectors,
false, false)
- val f = resolver.getEvaluator(parameterInfo)
- f -> f.init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors)
+ private lazy val inputDataTypes: Array[DataType] =
children.map(_.dataType).toArray
+
+ private def newEvaluator(): GenericUDAFEvaluator = {
+ val parameterInfo = new
SimpleGenericUDAFParameterInfo(inputInspectors, false, false)
+ resolver.getEvaluator(parameterInfo)
}
+ // The UDAF evaluator used to consume raw input rows and produce partial
aggregation results.
@transient
- private lazy val function = functionAndInspector._1
+ private lazy val partial1ModeEvaluator = newEvaluator()
+ // Hive `ObjectInspector` used to inspect partial aggregation results.
--- End diff --
Partial aggregation result is aggregation buffer, right?
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