hvanhovell commented on code in PR #40796:
URL: https://github.com/apache/spark/pull/40796#discussion_r1181870460
##########
connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -1271,10 +1268,35 @@ class Dataset[T] private[sql] (
val colNames: Seq[String] = col1 +: cols
new RelationalGroupedDataset(
toDF(),
- colNames.map(colName => Column(colName).expr),
+ colNames.map(colName => Column(colName)),
proto.Aggregate.GroupType.GROUP_TYPE_GROUPBY)
}
+ /**
+ * (Scala-specific) Reduces the elements of this Dataset using the specified
binary function.
+ * The given `func` must be commutative and associative or the result may be
non-deterministic.
+ *
+ * @group action
+ * @since 3.5.0
+ */
+ def reduce(func: (T, T) => T): T = {
+ val list = this
+ .groupByKey(UdfUtils.groupAllUnderBoolTrue())(PrimitiveBooleanEncoder)
Review Comment:
Not sure if this is a stellar idea, the problem is that some aggregation
implementations are optimized for keyless aggregation. By adding a dummy key
(which is very hard to detect due to the use of a udf), we won't be able to use
these code paths. Can we try to use the ReduceAggregator directly?
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