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[SPARK-38570][SQL] Incorrect DynamicPartitionPruning caused by Literal #35878

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[SPARK-38570][SQL] Incorrect DynamicPartitionPruning caused by Literal #35878

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e2fa2ca
literal_dynamic_partition
mcdull-zhang Mar 16, 2022
29766bd
fix communication
mcdull-zhang Mar 17, 2022
74053cf
fix
mcdull-zhang Mar 24, 2022
029d3b8
Merge branch 'master' into literal_dynamic_partition
mcdull-zhang Mar 24, 2022
ea6046f
Update sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expr…
mcdull-zhang Mar 24, 2022
4a863d9
Update sql/core/src/test/scala/org/apache/spark/sql/DynamicPartitionP…
mcdull-zhang Mar 24, 2022
7194d54
Merge branch 'master' of github.com:mcdull-zhang/spark into literal_d…
mcdull-zhang Mar 24, 2022
6fb13ff
Merge branch 'literal_dynamic_partition' of github.com:mcdull-zhang/s…
mcdull-zhang Mar 24, 2022
704af7d
triger flow
mcdull-zhang Mar 24, 2022
408934f
triger flow
mcdull-zhang Mar 24, 2022
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literal_dynamic_partition
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@mcdull-zhang
mcdull-zhang committed Mar 16, 2022
commit e2fa2ca5a35ccfb26f146bcfa28e609dacdfa6ec
2 changes: 1 addition & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/execution/dynamicpruning/PartitionPruning.scala
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Expand Up @@ -65,7 +65,7 @@ object PartitionPruning extends Rule[LogicalPlan] with PredicateHelper {
case fs: HadoopFsRelation =>
val partitionColumns = AttributeSet(
l.resolve(fs.partitionSchema, fs.sparkSession.sessionState.analyzer.resolver))
if (resExp.references.subsetOf(partitionColumns)) {
if (!resExp.references.isEmpty && resExp.references.subsetOf(partitionColumns)) {
return Some(l)
} else {
None
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49 changes: 49 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DynamicPartitionPruningSuite.scala
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Expand Up @@ -1528,6 +1528,55 @@ abstract class DynamicPartitionPruningSuiteBase
}
}
}

test("SPARK-38570: Fix incorrect DynamicPartitionPruning caused by Literal") {
withSQLConf(SQLConf.DYNAMIC_PARTITION_PRUNING_ENABLED.key -> "true") {
withTable("fact1", "fact2", "dim") {
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@wangyum wangyum Mar 23, 2022

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We already have lots of built-in tables. Cloud we change the test query to:

SELECT f.store_id,
       f.date_id,
       s.state_province
FROM   (SELECT 4 AS store_id,
               date_id,
               product_id
        FROM   fact_sk
        WHERE  date_id >= 1300
        UNION ALL
        SELECT 5 AS store_id,
               date_id,
               product_id
        FROM   fact_stats
        WHERE  date_id <= 1000) f
       JOIN dim_store s
         ON f.store_id = s.store_id
WHERE  s.country = 'US'

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@cloud-fan cloud-fan Mar 23, 2022

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+1

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@mcdull-zhang mcdull-zhang Mar 24, 2022

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So embarrassing, I wanted to use these built-in tables before, but I didn't see table fact_stats.

val fact1 = Seq[(Int, String, String)](
(1, "a1", "part1"),
(3, "a3", "part1"),
(5, "a5", "part1")
)
fact1.toDF("joinCol", "otherCol", "partCol")
.write
.partitionBy("partCol")
.format(tableFormat)
.saveAsTable("fact1")

val fact2 = Seq[(Int, String, String)](
(2, "b2", "part1"),
(4, "b4", "part1"),
(6, "b6", "part1")
)
fact2.toDF("joinCol", "otherCol", "partCol")
.write
.partitionBy("partCol")
.format(tableFormat)
.saveAsTable("fact2")

val dim = Seq[(String, Int, Int)](
("type1", 1, 100),
("type2", 2, 200)
)
dim.toDF("type", "joinCol", "score")
.write
.format(tableFormat)
.saveAsTable("dim")

val df = sql(
"""
|SELECT a.type,a.joinCol,a.otherCol,b.score FROM
|(SELECT 'type1' as type,joinCol,otherCol FROM fact1 WHERE partCol='part1'
|UNION ALL
|SELECT 'type2' as type,joinCol,otherCol FROM fact2 WHERE partCol='part1') a
|Join dim b ON a.type=b.type AND a.joinCol=b.joinCol;
|""".stripMargin)

checkPartitionPruningPredicate(df, false, withBroadcast = false)
checkAnswer(df, Row("type1", 1, "a1", 100) :: Row("type2", 2, "b2", 200) :: Nil)
}
}
}
}

abstract class DynamicPartitionPruningDataSourceSuiteBase
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