viirya commented on a change in pull request #26509: [SPARK-29427][SQL] Add API
to convert RelationalGroupedDataset to KeyValueGroupedDataset
URL: https://github.com/apache/spark/pull/26509#discussion_r348198305
##########
File path: sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
##########
@@ -2221,4 +2222,51 @@ class DataFrameSuite extends QueryTest with
SharedSparkSession {
val idTuples = sampled.collect().map(row => row.getLong(0) ->
row.getLong(1))
assert(idTuples.length == idTuples.toSet.size)
}
+
+ test("groupBy.keyAs") {
+ val df1 = Seq((1, 2, 3), (2, 3, 4)).toDF("a", "b", "c")
+ .repartition($"a", $"b").sortWithinPartitions("a", "b")
+ val df2 = Seq((1, 2, 4), (2, 3, 5)).toDF("a", "b", "c")
+ .repartition($"a", $"b").sortWithinPartitions("a", "b")
+
+ implicit val valueEncoder = RowEncoder(df1.schema)
+
+ val df3 = df1.groupBy("a", "b").as[GroupByKey, Row]
+ .cogroup(df2.groupBy("a", "b").as[GroupByKey, Row]) { case (_, data1,
data2) =>
+ data1.zip(data2).map { p =>
+ p._1.getInt(2) + p._2.getInt(2)
+ }
+ }.toDF
+
+ checkAnswer(df3.sort("value"), Row(7) :: Row(9) :: Nil)
+
+ // Assert that no extra shuffle introduced by cogroup.
+ val exchanges = df3.queryExecution.executedPlan.collect {
+ case h: ShuffleExchangeExec => h
+ }
+ assert(exchanges.size == 2)
+ }
+
+ test("groupBy.keyAs: custom grouping expressions") {
+ val df1 = Seq((1, 2, 3), (2, 3, 4)).toDF("a1", "b", "c")
+ .repartition($"a1", $"b").sortWithinPartitions("a1", "b")
+ val df2 = Seq((1, 2, 4), (2, 3, 5)).toDF("a1", "b", "c")
+ .repartition($"a1", $"b").sortWithinPartitions("a1", "b")
Review comment:
our distribution/partitioning framework is not enough smart to de-aliase and
match output partitioning and required distribution.
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