xupefei commented on code in PR #49111:
URL: https://github.com/apache/spark/pull/49111#discussion_r1942489244
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connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala:
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@@ -471,16 +481,120 @@ private class KeyValueGroupedDatasetImpl[K, V, IK, IV](
}
}
+ private def aggUntypedWithValueMapFunc(columns: TypedColumn[_, _]*):
Dataset[_] = {
+ val originalDs = sparkSession.newDataset(ivEncoder, plan)
+
+ // Apply the value transformation, get a DS of two columns "iv" and "v".
+ // If any of "iv" or "v" consists of a single primitive field, wrap it
with a struct so it
+ // would not be flattened.
+ // Also here we detect if the input "iv" is a single field struct. If yes,
we rename the field
+ // to "key" to align with Spark behaviour.
+ val (valueTransformedDf, ivFields, vFields) =
+ renameSingleFieldStruct(applyValueMapFunc(originalDs))
+
+ // Rewrite grouping expressions to use "iv" as input.
+ val updatedGroupingExprs = groupingColumns
+ .filterNot(c => KeyValueGroupedDatasetImpl.containsDummyUDF(c.node))
+ .map(c =>
+ ColumnNodeToProtoConverter.toExprWithTransformation(
+ c.node,
+ encoder = None,
+ rewriteInputColumnHook("iv", ivFields)))
+ // Rewrite aggregate columns to use "v" as input.
+ val updatedAggTypedExprs = columns.map { c =>
+ ColumnNodeToProtoConverter.toExprWithTransformation(
+ c.node,
+ encoder = Some(vEncoder), // Pass encoder to convert it to a typed
column.
+ rewriteInputColumnHook("v", vFields))
+ }
+
+ val rEnc = ProductEncoder.tuple(kEncoder +: columns.map(c =>
agnosticEncoderFor(c.encoder)))
+ sparkSession.newDataset(rEnc) { builder =>
+ builder.getAggregateBuilder
+ .setInput(valueTransformedDf.plan.getRoot)
+ .setGroupType(proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
+ .addAllGroupingExpressions(updatedGroupingExprs.asJava)
+ .addAllAggregateExpressions(updatedAggTypedExprs.asJava)
+ }
+ }
+
+ private def applyValueMapFunc(ds: Dataset[IV]): DataFrame = {
+ require(valueMapFunc.isDefined, "valueMapFunc is not defined")
+
+ val ivIsStruct = ivEncoder.isInstanceOf[StructEncoder[_]]
+ val vIsStruct = vEncoder.isInstanceOf[StructEncoder[_]]
+ val transformEncoder = {
+ val wrappedIvEncoder =
+ (if (ivIsStruct) ivEncoder else ProductEncoder.tuple(Seq(ivEncoder)))
+ .asInstanceOf[AgnosticEncoder[Any]]
+ val wrappedVEncoder =
+ (if (vIsStruct) vEncoder else ProductEncoder.tuple(Seq(vEncoder)))
+ .asInstanceOf[AgnosticEncoder[Any]]
+ ProductEncoder
+ .tuple(Seq(wrappedIvEncoder, wrappedVEncoder))
+ .asInstanceOf[AgnosticEncoder[(Any, Any)]]
+ }
+ val transformFunc = UDFAdaptors.mapValues(valueMapFunc.get, ivIsStruct,
vIsStruct)
+ ds.mapPartitions(transformFunc)(transformEncoder).toDF("iv", "v")
+ }
+
+ /**
+ * Given a DF of two Struct columns "iv" and "v", rename the fields of "iv"
if it consists of a
+ * single field. Also return the column names of "iv" and "v" to avoid
recomputing them later.
+ * @return
+ * (new dataframe, column names in IV, column names in V)
+ */
+ private def renameSingleFieldStruct(df: DataFrame): (DataFrame, Seq[String],
Seq[String]) = {
+ val ivSchema = df.schema(0).dataType.asInstanceOf[StructType]
Review Comment:
I am able to check if the ivEncoder is for a primitive type and rename it in
this case. The remaining question is how do we know the nullability? I need to
build a new StructType to rename the nested field.
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