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[SPARK-32163][SQL] Nested pruning should work even with cosmetic variations #28988

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viirya wants to merge 4 commits into from
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[SPARK-32163][SQL] Nested pruning should work even with cosmetic variations #28988

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4c047a1
nested pruning should work even with cosmetic variations.
viirya Jul 3, 2020
5f111b4
Add test case.
viirya Jul 5, 2020
04f6bb6
Remove unnessary cast.
viirya Jul 5, 2020
d352dbc
Fix ident.
viirya Jul 7, 2020
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11 changes: 8 additions & 3 deletions ...atalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NestedColumnAliasing.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -149,10 +149,12 @@ object NestedColumnAliasing {
case _ => false
}

// Note that when we group by extractors with their references, we should remove
// cosmetic variations.
val exclusiveAttrSet = AttributeSet(exclusiveAttrs ++ otherRootReferences)
val aliasSub = nestedFieldReferences.asInstanceOf[Seq[ExtractValue]]
.filter(!_.references.subsetOf(exclusiveAttrSet))
.groupBy(_.references.head)
.groupBy(_.references.head.canonicalized.asInstanceOf[Attribute])
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@maropu maropu Jul 5, 2020

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Nice catch, @viirya @frankyin-factual !

.flatMap { case (attr, nestedFields: Seq[ExtractValue]) =>
// Remove redundant `ExtractValue`s if they share the same parent nest field.
// For example, when `a.b` and `a.b.c` are in project list, we only need to alias `a.b`.
Expand All @@ -174,9 +176,12 @@ object NestedColumnAliasing {

// If all nested fields of `attr` are used, we don't need to introduce new aliases.
// By default, ColumnPruning rule uses `attr` already.
// Note that we need to remove cosmetic variations first, so we only count a
// nested field once.
if (nestedFieldToAlias.nonEmpty &&
nestedFieldToAlias
.map { case (nestedField, _) => totalFieldNum(nestedField.dataType) }
dedupNestedFields.map(_.canonicalized)
.distinct
.map { nestedField => totalFieldNum(nestedField.dataType) }
.sum < totalFieldNum(attr.dataType)) {
Some(attr.exprId -> nestedFieldToAlias)
} else {
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20 changes: 20 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/SchemaPruningSuite.scala
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Expand Up @@ -497,6 +497,26 @@ abstract class SchemaPruningSuite
Row(Row("Janet", null, "Jones"), "Jones") ::Nil)
}

testSchemaPruning("SPARK-32163: nested pruning should work even with cosmetic variations") {
withTempView("contact_alias") {
sql("select * from contacts")
.repartition(100, col("name.first"), col("name.last"))
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@maropu maropu Jul 5, 2020

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This issue cannot happen in branch-3.0?

spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NestedColumnAliasing.scala

Lines 80 to 85 in fc2660c

private def canProjectPushThrough(plan: LogicalPlan) = plan match {
case _: GlobalLimit => true
case _: LocalLimit => true
case _: Repartition => true
case _: Sample => true
case _ => false

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@viirya viirya Jul 5, 2020 •
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This test case is only for master branch. However this issue can happen in branch-3.0 too. I added another new test here, which is for branch-3.0.

But when we backport this to branch-3.0, we need to remove first test case as it will fail on checkScan(query, "struct<name:struct<first:string,last:string>>"), because branch-3.0 doesn't prune for repartition by expression.

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@maropu maropu Jul 5, 2020

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Yea, thanks for adding the test.

.selectExpr("name").createOrReplaceTempView("contact_alias")

val query1 = sql("select name.first from contact_alias")
checkScan(query1, "struct<name:struct<first:string,last:string>>")
checkAnswer(query1, Row("Jane") :: Row("John") :: Row("Jim") :: Row("Janet") ::Nil)

sql("select * from contacts")
.select(explode(col("friends.first")), col("friends"))
.createOrReplaceTempView("contact_alias")

val query2 = sql("select friends.middle, col from contact_alias")
checkScan(query2, "struct<friends:array<struct<first:string,middle:string>>>")
checkAnswer(query2, Row(Array("Z."), "Susan") :: Nil)
Comment on lines +514 to +516
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@viirya viirya Jul 5, 2020

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@maropu This test is for branch-3.0.

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@dongjoon-hyun dongjoon-hyun Jul 7, 2020

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Thank you for the test case.

}
}

protected def testSchemaPruning(testName: String)(testThunk: => Unit): Unit = {
test(s"Spark vectorized reader - without partition data column - $testName") {
withSQLConf(vectorizedReaderEnabledKey -> "true") {
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