hvanhovell commented on code in PR #40796:
URL: https://github.com/apache/spark/pull/40796#discussion_r1183916217
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connector/connect/server/src/main/scala/org/apache/spark/sql/connect/planner/SparkConnectPlanner.scala:
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@@ -664,7 +665,53 @@ class SparkConnectPlanner(val session: SparkSession) {
input: proto.Relation,
groupingExprs: java.util.List[proto.Expression],
sortingExprs: java.util.List[proto.Expression]):
UntypedKeyValueGroupedDataset = {
- val logicalPlan = transformRelation(input)
+ apply(transformRelation(input), groupingExprs, sortingExprs)
+ }
+
+ private def apply(
+ logicalPlan: LogicalPlan,
+ groupingExprs: java.util.List[proto.Expression],
+ sortingExprs: java.util.List[proto.Expression]):
UntypedKeyValueGroupedDataset = {
+ if (groupingExprs.size() == 1) {
+ createFromGroupByKeyFunc(logicalPlan, groupingExprs, sortingExprs)
+ } else if (groupingExprs.size() > 1) {
+ createFromRelationalDataset(logicalPlan, groupingExprs, sortingExprs)
+ } else {
+ throw InvalidPlanInput(
+ "The grouping expression cannot be absent for
KeyValueGroupedDataset")
+ }
+ }
+
+ private def createFromRelationalDataset(
+ logicalPlan: LogicalPlan,
+ groupingExprs: java.util.List[proto.Expression],
+ sortingExprs: java.util.List[proto.Expression]):
UntypedKeyValueGroupedDataset = {
+ assert(groupingExprs.size() >= 1)
+ val dummyFunc =
unpackUdf(groupingExprs.get(0).getCommonInlineUserDefinedFunction)
+ val groupExprs = groupingExprs.asScala.toSeq.drop(1).map(expr =>
transformExpression(expr))
+
+ val vEnc = ExpressionEncoder(dummyFunc.inputEncoders.head)
+ val kEnc = ExpressionEncoder(dummyFunc.outputEncoder)
+
+ val (qe, aliasedGroupings) =
Review Comment:
As a general guideline, the more we can avoid intermediate
Dataset/QueryExecution objects the better. The `analyzer` should take care of
most of the work.
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