Github user dilipbiswal commented on a diff in the pull request:
https://github.com/apache/spark/pull/17713#discussion_r115136438
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
---
@@ -414,4 +352,269 @@ trait CheckAnalysis extends PredicateHelper {
plan.foreach(_.setAnalyzed())
}
+
+ /**
+ * Validates subquery expressions in the plan. Upon failure, returns an
user facing error.
+ */
+ private def checkSubqueryExpression(plan: LogicalPlan, expr:
SubqueryExpression): Unit = {
+ def checkAggregate(conditions: Seq[Expression], query: LogicalPlan,
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.
+ 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))
+ // Collect the local references from the correlated predicate in the
subquery.
+ val subqueryColumns =
getCorrelatedPredicates(query).flatMap(_.references)
+ .filterNot(conditions.flatMap(_.references).contains)
+ 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(","))
+ }
+ }
+
+ // Skip subquery aliases added by the Analyzer.
+ // 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 => cleanQuery(p.child)
+ case child => child
+ }
+
+ // Validate to make sure the correlations appearing in the query are
valid and
+ // allowed by spark.
+ checkCorrelationsInSubquery(expr.plan)
+
+ expr match {
+ case ScalarSubquery(query, conditions, _) =>
+ // Scalar subquery must return one column as output.
+ if (query.output.size != 1) {
+ failAnalysis(
+ s"Scalar subquery must return only one column, but got
${query.output.size}")
+ }
+
+ if (conditions.nonEmpty) {
+ cleanQuery(query) match {
+ case a: Aggregate => checkAggregate(conditions, query, a)
+ case Filter(_, a: Aggregate) => checkAggregate(conditions,
query, a)
+ case fail => failAnalysis(s"Correlated scalar subqueries must
be Aggregated: $fail")
+ }
+
+ // Only certain operators are allowed to host subquery
expression containing
+ // outer references.
+ plan match {
+ case _: Filter | _: Aggregate | _: Project => // Ok
+ case other => failAnalysis(
+ s"Correlated scalar sub-queries can only be used in a " +
+ s"Filter/Aggregate/Project: $plan")
+ }
+ }
+
+ case inOrExistsSubquery =>
+ plan match {
+ case _: Filter => // Ok
+ case _ => failAnalysis(s"Predicate sub-queries can only be used
in a Filter: $plan")
--- End diff --
@gatorsmile changed.
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