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[SPARK-47569][SQL] Disallow comparing variant. #45726

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[SPARK-47569][SQL] Disallow comparing variant. #45726

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c66f671
initial
chenhao-db Mar 26, 2024
d35571c
add tests
chenhao-db Mar 26, 2024
986776b
Merge branch 'master' into SPARK-47569
chenhao-db Mar 27, 2024
2325048
add GROUP_EXPRESSION_TYPE_IS_NOT_ORDERABLE back
chenhao-db Mar 27, 2024
7981683
Merge branch 'master' into SPARK-47569
chenhao-db Mar 28, 2024
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6 changes: 6 additions & 0 deletions common/utils/src/main/resources/error/error-classes.json
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Original file line number Diff line number Diff line change
Expand Up @@ -1390,6 +1390,12 @@
],
"sqlState" : "42805"
},
"GROUP_EXPRESSION_TYPE_IS_NOT_ORDERABLE" : {
"message" : [
"The expression <sqlExpr> cannot be used as a grouping expression because its data type <dataType> is not an orderable data type."
],
"sqlState" : "42822"
},
"HLL_INVALID_INPUT_SKETCH_BUFFER" : {
"message" : [
"Invalid call to <function>; only valid HLL sketch buffers are supported as inputs (such as those produced by the `hll_sketch_agg` function)."
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6 changes: 6 additions & 0 deletions docs/sql-error-conditions.md
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Expand Up @@ -846,6 +846,12 @@ GROUP BY `<index>` refers to an expression `<aggExpr>` that contains an aggregat

GROUP BY position `<index>` is not in select list (valid range is [1, `<size>`]).

### GROUP_EXPRESSION_TYPE_IS_NOT_ORDERABLE

[SQLSTATE: 42822](sql-error-conditions-sqlstates.html#class-42-syntax-error-or-access-rule-violation)

The expression `<sqlExpr>` cannot be used as a grouping expression because its data type `<dataType>` is not an orderable data type.

### HLL_INVALID_INPUT_SKETCH_BUFFER

[SQLSTATE: 22546](sql-error-conditions-sqlstates.html#class-22-data-exception)
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3 changes: 2 additions & 1 deletion sql/api/src/main/scala/org/apache/spark/sql/catalyst/expressions/OrderUtils.scala
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Expand Up @@ -16,14 +16,15 @@
*/
package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.types.{ArrayType, AtomicType, DataType, NullType, StructType, UserDefinedType}
import org.apache.spark.sql.types.{ArrayType, AtomicType, DataType, NullType, StructType, UserDefinedType, VariantType}

object OrderUtils {
/**
* Returns true iff the data type can be ordered (i.e. can be sorted).
*/
def isOrderable(dataType: DataType): Boolean = dataType match {
case NullType => true
case VariantType => false
case dt: AtomicType => true
case struct: StructType => struct.fields.forall(f => isOrderable(f.dataType))
case array: ArrayType => isOrderable(array.elementType)
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11 changes: 10 additions & 1 deletion sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExprUtils.scala
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Expand Up @@ -28,7 +28,7 @@ import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
import org.apache.spark.sql.catalyst.plans.logical.Aggregate
import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, CharVarcharUtils}
import org.apache.spark.sql.errors.{QueryCompilationErrors, QueryErrorsBase, QueryExecutionErrors}
import org.apache.spark.sql.types.{DataType, MapType, StringType, StructType}
import org.apache.spark.sql.types.{DataType, MapType, StringType, StructType, VariantType}
import org.apache.spark.unsafe.types.UTF8String

object ExprUtils extends QueryErrorsBase {
Expand Down Expand Up @@ -193,6 +193,15 @@ object ExprUtils extends QueryErrorsBase {
messageParameters = Map("sqlExpr" -> expr.sql))
}

// Check if the data type of expr is orderable.
if (expr.dataType.existsRecursively(_.isInstanceOf[VariantType])) {
expr.failAnalysis(
errorClass = "GROUP_EXPRESSION_TYPE_IS_NOT_ORDERABLE",
messageParameters = Map(
"sqlExpr" -> toSQLExpr(expr),
"dataType" -> toSQLType(expr.dataType)))
}

if (!expr.deterministic) {
// This is just a sanity check, our analysis rule PullOutNondeterministic should
// already pull out those nondeterministic expressions and evaluate them in
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29 changes: 29 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/VariantSuite.scala
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Expand Up @@ -251,4 +251,33 @@ class VariantSuite extends QueryTest with SharedSparkSession {
}
}
}

test("group/order/join variant are disabled") {
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Thanks for the tests!

What about sort by? or window partition by, or window order by? Some examples:

select parse_json('') sort by 1

with t as (select 1 as a, parse_json('') as v)
select rank() over (partition by v order by a)

with t as (select 1 as a, parse_json('') as v)
select rank() over (partition by a order by v)

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@chenhao-db chenhao-db Mar 26, 2024 •
edited
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This is a very good point. Actually, the second statement (partition by v) will not fail as expected, and it is really an error in the type check for Window. Both partitionSpec and orderSpec must be orderable, but the current type check only includes orderSpec. If the partitionSpec contains a map type, the query will fail later in an optimizer rule with INTERNAL_ERROR. I will create another PR to fix the type check for Window.

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If we can merge #45730 before this PR, we can add the second statement to the tests.

var ex = intercept[AnalysisException] {
spark.sql("select parse_json('') group by 1")
}
assert(ex.getErrorClass == "GROUP_EXPRESSION_TYPE_IS_NOT_ORDERABLE")

ex = intercept[AnalysisException] {
spark.sql("select parse_json('') order by 1")
}
assert(ex.getErrorClass == "DATATYPE_MISMATCH.INVALID_ORDERING_TYPE")

ex = intercept[AnalysisException] {
spark.sql("select parse_json('') sort by 1")
}
assert(ex.getErrorClass == "DATATYPE_MISMATCH.INVALID_ORDERING_TYPE")

ex = intercept[AnalysisException] {
spark.sql("with t as (select 1 as a, parse_json('') as v) " +
"select rank() over (partition by a order by v) from t")
}
assert(ex.getErrorClass == "DATATYPE_MISMATCH.INVALID_ORDERING_TYPE")

ex = intercept[AnalysisException] {
spark.sql("with t as (select parse_json('') as v) " +
"select t1.v from t as t1 join t as t2 on t1.v = t2.v")
}
assert(ex.getErrorClass == "DATATYPE_MISMATCH.INVALID_ORDERING_TYPE")
}
}