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

JIRA: https://issues.apache.org/jira/browse/SPARK-11949

The result of cube plan uses incorrect schema. The schema of cube result should set nullable property to true because the grouping expressions will have null values.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #10038 from viirya/fix-cube.

2 files changed, 18 insertions, 2 deletions

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 94ffbbb2e5..b8f212fca7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -223,6 +223,11 @@ class Analyzer(
           case other => Alias(other, other.toString)()
         }
 
+        // TODO: We need to use bitmasks to determine which grouping expressions need to be
+        // set as nullable. For example, if we have GROUPING SETS ((a,b), a), we do not need
+        // to change the nullability of a.
+        val attributeMap = groupByAliases.map(a => (a -> a.toAttribute.withNullability(true))).toMap
+
         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
@@ -231,12 +236,13 @@ 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,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index b5c636d0de..b1004bc5bc 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -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._
@@ -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") {