jingz-db commented on code in PR #49488:
URL: https://github.com/apache/spark/pull/49488#discussion_r1951698877
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sql/connect/common/src/main/scala/org/apache/spark/sql/connect/KeyValueGroupedDataset.scala:
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@@ -526,6 +553,71 @@ private class KeyValueGroupedDatasetImpl[K, V, IK, IV](
}
}
+ override protected[sql] def transformWithStateHelper[U: Encoder, S: Encoder](
+ statefulProcessor: StatefulProcessor[K, V, U],
+ timeMode: TimeMode,
+ outputMode: OutputMode,
+ initialState: Option[sql.KeyValueGroupedDataset[K, S]] = None,
+ eventTimeColumnName: String = ""): Dataset[U] = {
+ val outputEncoder = agnosticEncoderFor[U]
+ val stateEncoder = agnosticEncoderFor[S]
+
+ val inputEncoders: Seq[AgnosticEncoder[_]] = Seq(kEncoder, stateEncoder,
ivEncoder)
+ val dummyGroupingFunc = SparkUserDefinedFunction(
Review Comment:
Please ignore my comments above. I found `UdfPacket` in the
CommonInlineUserDefinedFunction is doing java serialization on `AnyRef` type
similar to what we are trying to do here for `StatefulProcessor`. So I remove
the dummy udf implementation above and take advantage of `UdfPacket` instead.
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