jingz-db commented on code in PR #49488:
URL: https://github.com/apache/spark/pull/49488#discussion_r1947035847
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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:
This dummy UDF is to pass over `AgnosticEncoder` of `K, U, S` to
`SparkConnectPlanner` in a dummy no-op UDF. I got the idea from here:
https://github.com/apache/spark/blob/master/sql/connect/common/src/main/scala/org/apache/spark/sql/connect/KeyValueGroupedDataset.scala#L696.
In spark connect, `KeyValueGroupedDataset` was passed to connect server and
resolved as `UntypedKeyValueGroupedDataset`. So on spark connect server, we
will need to get the Scala type from `AgnosticEncoder`. In `GroupMap` type
defined in proto message
[here](https://github.com/apache/spark/blob/master/sql/connect/common/src/main/protobuf/spark/connect/relations.proto#L1011),
`AgnosticEncoder` of input key type `K`, output type `U` are wrapped in
`CommonInlineUserDefinedFunction` object. As TWS is also one of the operator in
this `GroupMap`, I chose to transmit the encoders in this no-op UDF. On spark
connect server side, we will only be reading out the encoders from `func`, the
function in the dummy udf will not be used.
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