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[SPARK-11949][SQL] Set field nullable property for GroupingSets to get correct results for null values #10038

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[SPARK-11949][SQL] Set field nullable property for GroupingSets to get correct results for null values #10038

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0f335a6
Fix wrong cube results.
viirya Nov 30, 2015
c3c37fe
Fix scala style.
viirya Nov 30, 2015
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7 changes: 5 additions & 2 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -223,6 +223,8 @@ class Analyzer(
case other => Alias(other, other.toString)()
}

val attributeMap = groupByAliases.map(a => (a -> a.toAttribute.withNullability(true))).toMap
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@yhuai yhuai Nov 30, 2015

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Maybe we need to check the bitmasks? I mean if we something like GROUPING SETS ( (a,b), a), we do not need to change the nullability of a, right?

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@yhuai yhuai Nov 30, 2015

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(this is a minor issue since setting nullable to true does not cause wrong results)

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@viirya viirya Nov 30, 2015

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Indeed, I think you are right. I will update this later. Thanks.

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@viirya viirya Nov 30, 2015

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Yeah, that is right too.

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@yhuai yhuai Dec 1, 2015

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Do you want to update it in this PR?

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@viirya viirya Dec 1, 2015

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This can be merged first. I will add a following one for the issue later. If it is ok for you.

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@yhuai yhuai Dec 1, 2015

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OK. Sounds good. I will add a TODO at here when I merge it.

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@marmbrus marmbrus Dec 1, 2015

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Friendly ping, if we can fix this last comment we can probably include this in 1.6-RC1.


val aggregations: Seq[NamedExpression] = x.aggregations.map {
// If an expression is an aggregate (contains a AggregateExpression) then we dont change
// it so that the aggregation is computed on the unmodified value of its argument
Expand All @@ -231,12 +233,13 @@ class Analyzer(
// If not then its a grouping expression and we need to use the modified (with nulls from
// Expand) value of the expression.
case expr => expr.transformDown {
case e => groupByAliases.find(_.child.semanticEquals(e)).map(_.toAttribute).getOrElse(e)
case e =>
groupByAliases.find(_.child.semanticEquals(e)).map(attributeMap(_)).getOrElse(e)
}.asInstanceOf[NamedExpression]
}

val child = Project(x.child.output ++ groupByAliases, x.child)
val groupByAttributes = groupByAliases.map(_.toAttribute)
val groupByAttributes = groupByAliases.map(attributeMap(_))

Aggregate(
groupByAttributes :+ VirtualColumn.groupingIdAttribute,
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10 changes: 10 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
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Expand Up @@ -21,6 +21,7 @@ import org.apache.spark.sql.functions._
import org.apache.spark.sql.test.SharedSQLContext
import org.apache.spark.sql.types.DecimalType

case class Fact(date: Int, hour: Int, minute: Int, room_name: String, temp: Double)

class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
import testImplicits._
Expand Down Expand Up @@ -86,6 +87,15 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
Row(null, 2013, 78000.0) ::
Row(null, null, 113000.0) :: Nil
)

val df0 = sqlContext.sparkContext.parallelize(Seq(
Fact(20151123, 18, 35, "room1", 18.6),
Fact(20151123, 18, 35, "room2", 22.4),
Fact(20151123, 18, 36, "room1", 17.4),
Fact(20151123, 18, 36, "room2", 25.6))).toDF()

val cube0 = df0.cube("date", "hour", "minute", "room_name").agg(Map("temp" -> "avg"))
assert(cube0.where("date IS NULL").count > 0)
}

test("rollup overlapping columns") {
Expand Down