cloud-fan commented on a change in pull request #34738:
URL: https://github.com/apache/spark/pull/34738#discussion_r768923388
##########
File path: sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCV2Suite.scala
##########
@@ -120,56 +122,119 @@ class JDBCV2Suite extends QueryTest with
SharedSparkSession with ExplainSuiteHel
.table("h2.test.employee")
.filter($"dept" > 1)
.limit(1)
- checkPushedLimit(df2, true, 1)
+ checkPushedLimit(df2, Some(1))
checkAnswer(df2, Seq(Row(2, "alex", 12000.00, 1200.0)))
val df3 = sql("SELECT name FROM h2.test.employee WHERE dept > 1 LIMIT 1")
val scan = df3.queryExecution.optimizedPlan.collectFirst {
case s: DataSourceV2ScanRelation => s
}.get
assert(scan.schema.names.sameElements(Seq("NAME")))
- checkPushedLimit(df3, true, 1)
+ checkPushedLimit(df3, Some(1))
checkAnswer(df3, Seq(Row("alex")))
val df4 = spark.read
.table("h2.test.employee")
.groupBy("DEPT").sum("SALARY")
.limit(1)
- checkPushedLimit(df4, false, 0)
+ checkPushedLimit(df4, None)
checkAnswer(df4, Seq(Row(1, 19000.00)))
- val df5 = spark.read
- .table("h2.test.employee")
- .sort("SALARY")
- .limit(1)
- checkPushedLimit(df5, false, 0)
- checkAnswer(df5, Seq(Row(1, "cathy", 9000.00, 1200.0)))
-
val name = udf { (x: String) => x.matches("cat|dav|amy") }
val sub = udf { (x: String) => x.substring(0, 3) }
- val df6 = spark.read
+ val df5 = spark.read
.table("h2.test.employee")
.select($"SALARY", $"BONUS", sub($"NAME").as("shortName"))
.filter(name($"shortName"))
.limit(1)
// LIMIT is pushed down only if all the filters are pushed down
- checkPushedLimit(df6, false, 0)
- checkAnswer(df6, Seq(Row(10000.00, 1000.0, "amy")))
+ checkPushedLimit(df5, None)
+ checkAnswer(df5, Seq(Row(10000.00, 1000.0, "amy")))
}
- private def checkPushedLimit(df: DataFrame, pushed: Boolean, limit: Int):
Unit = {
+ private def checkPushedLimit(df: DataFrame, limit: Option[Int] = None,
+ sortValues: Seq[SortValue] = Nil): Unit = {
df.queryExecution.optimizedPlan.collect {
case relation: DataSourceV2ScanRelation => relation.scan match {
case v1: V1ScanWrapper =>
- if (pushed) {
- assert(v1.pushedDownOperators.limit === Some(limit))
- } else {
- assert(v1.pushedDownOperators.limit.isEmpty)
- }
+ assert(v1.pushedDownOperators.limit === limit)
+ assert(v1.pushedDownOperators.sortValues === sortValues)
}
}
}
+ test("simple scan with top N") {
+ val df1 = spark.read
+ .table("h2.test.employee")
+ .sort("salary")
+ .limit(1)
+ checkPushedLimit(df1, Some(1), createSortValues())
+ checkAnswer(df1, Seq(Row(1, "cathy", 9000.00, 1200.0)))
+
+ val df2 = spark.read.table("h2.test.employee")
+ .where($"dept" === 1).orderBy($"salary").limit(1)
+ checkPushedLimit(df2, Some(1), createSortValues())
+ checkAnswer(df2, Seq(Row(1, "cathy", 9000.00, 1200.0)))
+
+ val df3 = spark.read
+ .option("partitionColumn", "dept")
+ .option("lowerBound", "0")
+ .option("upperBound", "2")
+ .option("numPartitions", "2")
+ .table("h2.test.employee")
+ .filter($"dept" > 1)
+ .orderBy($"salary".desc)
+ .limit(1)
+ checkPushedLimit(
+ df3, Some(1), createSortValues(SortDirection.DESCENDING,
NullOrdering.NULLS_LAST))
+ checkAnswer(df3, Seq(Row(2, "alex", 12000.00, 1200.0)))
+
+ val df4 =
+ sql("SELECT name FROM h2.test.employee WHERE dept > 1 ORDER BY salary
NULLS LAST LIMIT 1")
+ val scan = df4.queryExecution.optimizedPlan.collectFirst {
+ case s: DataSourceV2ScanRelation => s
+ }.get
+ assert(scan.schema.names.sameElements(Seq("NAME", "SALARY")))
+ checkPushedLimit(df4, Some(1), createSortValues(nullOrdering =
NullOrdering.NULLS_LAST))
+ checkAnswer(df4, Seq(Row("david")))
+
+ val df5 = spark.read.table("h2.test.employee")
+ .where($"dept" === 1).orderBy($"salary")
+ checkPushedLimit(df5, None, Seq.empty)
+ checkAnswer(df5, Seq(Row(1, "cathy", 9000.00, 1200.0), Row(1, "amy",
10000.00, 1000.0)))
+
+ val df6 = spark.read.table("h2.test.employee")
+ .where($"dept" === 1).limit(1)
Review comment:
This has been covered by the limit pushdown tests already.
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