spark-instrumented-optimizer/sql/core/src
Herman van Hovell 83061be697 [SPARK-14858] [SQL] Enable subquery pushdown
The previous subquery PRs did not include support for pushing subqueries used in filters (`WHERE`/`HAVING`) down. This PR adds this support. For example :
```scala
range(0, 10).registerTempTable("a")
range(5, 15).registerTempTable("b")
range(7, 25).registerTempTable("c")
range(3, 12).registerTempTable("d")
val plan = sql("select * from a join b on a.id = b.id left join c on c.id = b.id where a.id in (select id from d)")
plan.explain(true)
```
Leads to the following Analyzed & Optimized plans:
```
== Parsed Logical Plan ==
...

== Analyzed Logical Plan ==
id: bigint, id: bigint, id: bigint
Project [id#0L,id#4L,id#8L]
+- Filter predicate-subquery#16 [(id#0L = id#12L)]
   :  +- SubqueryAlias predicate-subquery#16 [(id#0L = id#12L)]
   :     +- Project [id#12L]
   :        +- SubqueryAlias d
   :           +- Range 3, 12, 1, 8, [id#12L]
   +- Join LeftOuter, Some((id#8L = id#4L))
      :- Join Inner, Some((id#0L = id#4L))
      :  :- SubqueryAlias a
      :  :  +- Range 0, 10, 1, 8, [id#0L]
      :  +- SubqueryAlias b
      :     +- Range 5, 15, 1, 8, [id#4L]
      +- SubqueryAlias c
         +- Range 7, 25, 1, 8, [id#8L]

== Optimized Logical Plan ==
Join LeftOuter, Some((id#8L = id#4L))
:- Join Inner, Some((id#0L = id#4L))
:  :- Join LeftSemi, Some((id#0L = id#12L))
:  :  :- Range 0, 10, 1, 8, [id#0L]
:  :  +- Range 3, 12, 1, 8, [id#12L]
:  +- Range 5, 15, 1, 8, [id#4L]
+- Range 7, 25, 1, 8, [id#8L]

== Physical Plan ==
...
```
I have also taken the opportunity to move quite a bit of code around:
- Rewriting subqueris and pulling out correlated predicated from subqueries has been moved into the analyzer. The analyzer transforms `Exists` and `InSubQuery` into `PredicateSubquery` expressions. A PredicateSubquery exposes the 'join' expressions and the proper references. This makes things like type coercion, optimization and planning easier to do.
- I have added support for `Aggregate` plans in subqueries. Any correlated expressions will be added to the grouping expressions. I have removed support for `Union` plans, since pulling in an outer reference from beneath a Union has no value (a filtered value could easily be part of another Union child).
- Resolution of subqueries is now done using `OuterReference`s. These are used to wrap any outer reference; this makes the identification of these references easier, and also makes dealing with duplicate attributes in the outer and inner plans easier. The resolution of subqueries initially used a resolution loop which would alternate between calling the analyzer and trying to resolve the outer references. We now use a dedicated analyzer which uses a special rule for outer reference resolution.

These changes are a stepping stone for enabling correlated scalar subqueries, enabling all Hive tests & allowing us to use predicate subqueries anywhere.

Current tests and added test cases in FilterPushdownSuite.

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #12720 from hvanhovell/SPARK-14858.
2016-04-29 16:50:12 -07:00
..
main [SPARK-14988][PYTHON] SparkSession API follow-ups 2016-04-29 16:41:13 -07:00
test [SPARK-14858] [SQL] Enable subquery pushdown 2016-04-29 16:50:12 -07:00