Github user nsyca commented on a diff in the pull request:
https://github.com/apache/spark/pull/16572#discussion_r95981409
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
---
@@ -117,66 +117,72 @@ trait CheckAnalysis extends PredicateHelper {
failAnalysis(s"Window specification $s is not valid
because $m")
case None => w
}
- case s @ ScalarSubquery(query, conditions, _)
+
+ case e @ PredicateSubquery(query, _, _, _) =>
+ checkAnalysis(query)
+ e
+
+ case s @ ScalarSubquery(query, conditions, _) =>
// If no correlation, the output must be exactly one column
- if (conditions.isEmpty && query.output.size != 1) =>
+ if (conditions.isEmpty && query.output.size != 1) {
failAnalysis(
s"Scalar subquery must return only one column, but got
${query.output.size}")
-
- case s @ ScalarSubquery(query, conditions, _) if
conditions.nonEmpty =>
-
- // Collect the columns from the subquery for further checking.
- var subqueryColumns =
conditions.flatMap(_.references).filter(query.output.contains)
-
- def checkAggregate(agg: Aggregate): Unit = {
- // Make sure correlated scalar subqueries contain one row
for every outer row by
- // enforcing that they are aggregates which contain exactly
one aggregate expressions.
- // The analyzer has already checked that subquery contained
only one output column,
- // and added all the grouping expressions to the aggregate.
- val aggregates = agg.expressions.flatMap(_.collect {
- case a: AggregateExpression => a
- })
- if (aggregates.isEmpty) {
- failAnalysis("The output of a correlated scalar subquery
must be aggregated")
- }
-
- // SPARK-18504/SPARK-18814: Block cases where GROUP BY
columns
- // are not part of the correlated columns.
- val groupByCols =
AttributeSet(agg.groupingExpressions.flatMap(_.references))
- val correlatedCols = AttributeSet(subqueryColumns)
- val invalidCols = groupByCols -- correlatedCols
- // GROUP BY columns must be a subset of columns in the
predicates
- if (invalidCols.nonEmpty) {
- failAnalysis(
- "A GROUP BY clause in a scalar correlated subquery " +
- "cannot contain non-correlated columns: " +
- invalidCols.mkString(","))
- }
}
+ else if (conditions.nonEmpty) {
+ // Collect the columns from the subquery for further
checking.
+ var subqueryColumns =
conditions.flatMap(_.references).filter(query.output.contains)
+
+ def checkAggregate(agg: Aggregate): Unit = {
+ // Make sure correlated scalar subqueries contain one row
for every outer row by
+ // enforcing that they are aggregates containing exactly
one aggregate expression.
+ // The analyzer has already checked that subquery
contained only one output column,
+ // and added all the grouping expressions to the aggregate.
+ val aggregates = agg.expressions.flatMap(_.collect {
+ case a: AggregateExpression => a
+ })
+ if (aggregates.isEmpty) {
+ failAnalysis("The output of a correlated scalar subquery
must be aggregated")
+ }
- // Skip subquery aliases added by the Analyzer and the
SQLBuilder.
- // For projects, do the necessary mapping and skip to its
child.
- def cleanQuery(p: LogicalPlan): LogicalPlan = p match {
- case s: SubqueryAlias => cleanQuery(s.child)
- case p: Project =>
- // SPARK-18814: Map any aliases to their
AttributeReference children
- // for the checking in the Aggregate operators below this
Project.
- subqueryColumns = subqueryColumns.map {
- xs => p.projectList.collectFirst {
- case e @ Alias(child : AttributeReference, _) if
e.exprId == xs.exprId =>
- child
- }.getOrElse(xs)
+ // SPARK-18504/SPARK-18814: Block cases where GROUP BY
columns
+ // are not part of the correlated columns.
+ val groupByCols =
AttributeSet(agg.groupingExpressions.flatMap(_.references))
+ val correlatedCols = AttributeSet(subqueryColumns)
+ val invalidCols = groupByCols -- correlatedCols
+ // GROUP BY columns must be a subset of columns in the
predicates
+ if (invalidCols.nonEmpty) {
+ failAnalysis(
+ "A GROUP BY clause in a scalar correlated subquery " +
+ "cannot contain non-correlated columns: " +
+ invalidCols.mkString(","))
}
+ }
- cleanQuery(p.child)
- case child => child
- }
+ // Skip subquery aliases added by the Analyzer and the
SQLBuilder.
+ // For projects, do the necessary mapping and skip to its
child.
+ def cleanQuery(p: LogicalPlan): LogicalPlan = p match {
+ case s: SubqueryAlias => cleanQuery(s.child)
+ case p: Project =>
+ // SPARK-18814: Map any aliases to their
AttributeReference children
+ // for the checking in the Aggregate operators below
this Project.
+ subqueryColumns = subqueryColumns.map {
+ xs => p.projectList.collectFirst {
+ case e @ Alias(child : AttributeReference, _) if
e.exprId == xs.exprId =>
+ child
+ }.getOrElse(xs)
+ }
+
+ cleanQuery(p.child)
+ case child => child
+ }
- cleanQuery(query) match {
- case a: Aggregate => checkAggregate(a)
- case Filter(_, a: Aggregate) => checkAggregate(a)
- case fail => failAnalysis(s"Correlated scalar subqueries
must be Aggregated: $fail")
+ cleanQuery(query) match {
+ case a: Aggregate => checkAggregate(a)
+ case Filter(_, a: Aggregate) => checkAggregate(a)
+ case fail => failAnalysis(s"Correlated scalar subqueries
must be Aggregated: $fail")
+ }
}
+ checkAnalysis(query)
--- End diff --
The best way to view this block of code changes is using a `diff` with
`-b`. The main part is to call `checkAnalysis` for both PredicateSubquery and
ScalaSubquery.
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