Subqueries (Subquery Expressions)¶
As of Spark 2.0, Spark SQL supports subqueries.
A subquery (aka subquery expression) is a query that is nested inside of another query.
There are the following kinds of subqueries:
. A subquery as a source (inside a SQL
FROM clause) . A scalar subquery or a predicate subquery (as a column)
Every subquery can also be correlated or uncorrelated.
[[scalar-subquery]] A scalar subquery is a structured query that returns a single row and a single column only. Spark SQL uses ScalarSubquery (SubqueryExpression) expression to represent scalar subqueries (while sql/AstBuilder.md#visitSubqueryExpression[parsing a SQL statement]).
// FIXME: ScalarSubquery in a logical plan¶
ScalarSubquery expression appears as scalar-subquery#[exprId] [conditionString] in a logical plan.
// FIXME: Name of a ScalarSubquery in a logical plan¶
It is said that scalar subqueries should be used very rarely if at all and you should join instead.
Spark Analyzer uses ResolveSubquery resolution rule to resolve subqueries and at the end CheckAnalysis.md#checkSubqueryExpression[makes sure that they are valid].
Catalyst Optimizer uses the following optimizations for subqueries:
PullupCorrelatedPredicates.md[PullupCorrelatedPredicates] optimization to PullupCorrelatedPredicates.md#rewriteSubQueries[rewrite subqueries] and pull up correlated predicates
RewriteCorrelatedScalarSubquery optimization (to constructLeftJoins)
Spark Physical Optimizer uses PlanSubqueries physical optimization to plan queries with scalar subqueries.