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[SPARK-32906][SQL] Struct field names should not change after normalizing floats #29780

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[SPARK-32906][SQL] Struct field names should not change after normalizing floats #29780

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6 changes: 3 additions & 3 deletions ...yst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/NormalizeFloatingNumbers.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -129,10 +129,10 @@ object NormalizeFloatingNumbers extends Rule[LogicalPlan] {
Coalesce(children.map(normalize))

case _ if expr.dataType.isInstanceOf[StructType] =>
val fields = expr.dataType.asInstanceOf[StructType].fields.indices.map { i =>
normalize(GetStructField(expr, i))
val fields = expr.dataType.asInstanceOf[StructType].fieldNames.zipWithIndex.map {
case (name, i) => Seq(Literal(name), normalize(GetStructField(expr, i)))
}
val struct = CreateStruct(fields)
val struct = CreateNamedStruct(fields.flatten.toSeq)
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@HyukjinKwon HyukjinKwon Sep 18, 2020

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Looks logically fine but just to confirm that I understood correctly, it's just a cleanup fix to match the column names, right? and doesn't affect the final output?

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@cloud-fan cloud-fan Sep 18, 2020

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Yea I think so. CreateNamedStruct allows you to specify the name for each field.

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@maropu maropu Sep 18, 2020

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Yeah, you're right and this is not an user-facing bug. When writing code to check paln integrity for SparkPlans, I found this issue; canonicalized attributes between final/partial HaashAggregates were not equal incorrectly because of this issue (schema mismatch).

KnownFloatingPointNormalized(If(IsNull(expr), Literal(null, struct.dataType), struct))

case _ if expr.dataType.isInstanceOf[ArrayType] =>
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8 changes: 8 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
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Expand Up @@ -1043,6 +1043,14 @@ class DataFrameAggregateSuite extends QueryTest
checkAnswer(sql(queryTemplate("FIRST")), Row(1))
checkAnswer(sql(queryTemplate("LAST")), Row(3))
}

test("SPARK-32906: struct field names should not change after normalizing floats") {
val df = Seq(Tuple1(Tuple2(-0.0d, Double.NaN)), Tuple1(Tuple2(0.0d, Double.NaN))).toDF("k")
val aggs = df.distinct().queryExecution.sparkPlan.collect { case a: HashAggregateExec => a }
assert(aggs.length == 2)
assert(aggs.head.output.map(_.dataType.simpleString).head ===
aggs.last.output.map(_.dataType.simpleString).head)
}
}

case class B(c: Option[Double])
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