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[SPARK-39135][SQL] DS V2 aggregate partial push-down should supports group by without aggregate functions #36492

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[SPARK-39135][SQL] DS V2 aggregate partial push-down should supports group by without aggregate functions #36492

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2 changes: 1 addition & 1 deletion ...src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanRelationPushDown.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -294,7 +294,7 @@ object V2ScanRelationPushDown extends Rule[LogicalPlan] with PredicateHelper wit
private def supportPartialAggPushDown(agg: Aggregation): Boolean = {
// We don't know the agg buffer of `GeneralAggregateFunc`, so can't do partial agg push down.
// If `Sum`, `Count`, `Avg` with distinct, can't do partial agg push down.
agg.aggregateExpressions().exists {
agg.aggregateExpressions().isEmpty || agg.aggregateExpressions().exists {
case sum: Sum => !sum.isDistinct
case count: Count => !count.isDistinct
case avg: Avg => !avg.isDistinct
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51 changes: 51 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCV2Suite.scala
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Expand Up @@ -727,6 +727,57 @@ class JDBCV2Suite extends QueryTest with SharedSparkSession with ExplainSuiteHel
checkAnswer(df, Seq(Row(5)))
}

test("scan with aggregate push-down: GROUP BY without aggregate functions") {
val df = sql("select name FROM h2.test.employee GROUP BY name")
checkAggregateRemoved(df)
checkPushedInfo(df,
"PushedAggregates: [], PushedFilters: [], PushedGroupByExpressions: [NAME],")
checkAnswer(df, Seq(Row("alex"), Row("amy"), Row("cathy"), Row("david"), Row("jen")))

val df2 = spark.read
.option("partitionColumn", "dept")
.option("lowerBound", "0")
.option("upperBound", "2")
.option("numPartitions", "2")
.table("h2.test.employee")
.groupBy($"name")
.agg(Map.empty[String, String])
checkAggregateRemoved(df2, false)
checkPushedInfo(df2,
"PushedAggregates: [], PushedFilters: [], PushedGroupByExpressions: [NAME],")
checkAnswer(df2, Seq(Row("alex"), Row("amy"), Row("cathy"), Row("david"), Row("jen")))

val df3 = sql("SELECT CASE WHEN SALARY > 8000 AND SALARY < 10000 THEN SALARY ELSE 0 END as" +
" key FROM h2.test.employee GROUP BY key")
checkAggregateRemoved(df3)
checkPushedInfo(df3,
"""
|PushedAggregates: [],
|PushedFilters: [],
|PushedGroupByExpressions:
|[CASE WHEN (SALARY > 8000.00) AND (SALARY < 10000.00) THEN SALARY ELSE 0.00 END],
|""".stripMargin.replaceAll("\n", " "))
checkAnswer(df3, Seq(Row(0), Row(9000)))

val df4 = spark.read
.option("partitionColumn", "dept")
.option("lowerBound", "0")
.option("upperBound", "2")
.option("numPartitions", "2")
.table("h2.test.employee")
.groupBy(when(($"SALARY" > 8000).and($"SALARY" < 10000), $"SALARY").otherwise(0).as("key"))
.agg(Map.empty[String, String])
checkAggregateRemoved(df4, false)
checkPushedInfo(df4,
"""
|PushedAggregates: [],
|PushedFilters: [],
|PushedGroupByExpressions:
|[CASE WHEN (SALARY > 8000.00) AND (SALARY < 10000.00) THEN SALARY ELSE 0.00 END],
|""".stripMargin.replaceAll("\n", " "))
checkAnswer(df4, Seq(Row(0), Row(9000)))
}

test("scan with aggregate push-down: COUNT(col)") {
val df = sql("select COUNT(DEPT) FROM h2.test.employee")
checkAggregateRemoved(df)
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