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[SPARK-26138][SQL] Cross join requires push LocalLimit in LimitPushDown rule #23104

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[SPARK-26138][SQL] Cross join requires push LocalLimit in LimitPushDown rule #23104

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deb1809
[SPARK-26138][SQL] LimitPushDown cross join requires maybeBushLocalLimit
Nov 21, 2018
a0b9cb4
[SPARK-26138][SQL] add three UTs
Nov 22, 2018
588c151
[SPARK-26138][SQL] Push limit to the right side
Nov 23, 2018
e173962
[SPARK-26138][SQL] Fix the scala style. File line length exceeds 100 …
Dec 18, 2018
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1 change: 1 addition & 0 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
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Expand Up @@ -459,6 +459,7 @@ object LimitPushDown extends Rule[LogicalPlan] {
val newJoin = joinType match {
case RightOuter => join.copy(right = maybePushLocalLimit(exp, right))
case LeftOuter => join.copy(left = maybePushLocalLimit(exp, left))
case Cross => join.copy(left = maybePushLocalLimit(exp, left), right = maybePushLocalLimit(exp, right))
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@cloud-fan cloud-fan Nov 26, 2018

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how about inner join without condition?

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@cloud-fan cloud-fan Nov 26, 2018

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maybe we can match InnerLike when condition is empty.

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@guoxiaolongzte guoxiaolongzte Nov 26, 2018 •
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A = {(a, 0), (b, 1), (c, 2), (d, 0), (e, 1), (f, 2)}
B = {(e, 1), (f, 2)}

A inner join B limit 2
If there is limit 2, (a, 0), (b, 1) inner join {(e, 1), (f, 2)}, the result is empty. But the real result is not empty.

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@cloud-fan cloud-fan Nov 26, 2018

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inner join without condition is literally cross join.

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@guoxiaolongzte guoxiaolongzte Nov 26, 2018 •
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When set spark.sql.crossJoin.enabled=true,
inner join without condition, LeftOuter without condition, RightOuter without condition, FullOuter without condition， all these are iterally cross join?
@cloud-fan

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@gatorsmile gatorsmile Nov 26, 2018

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@guoxiaolongzte nope.

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@guoxiaolongzte guoxiaolongzte Nov 26, 2018 •
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I think, if when set spark.sql.crossJoin.enabled=true, if Inner join without condition, LeftOuter join without condition, RightOuter join without condition, FullOuter join without condition , limit should be pushed down on both sides, just like cross join limit in this PR.
Is this correct?

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@guoxiaolongzte guoxiaolongzte Nov 27, 2018

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@cloud-fan
Please give me some advice. Thank you.

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@cloud-fan cloud-fan Nov 27, 2018 •
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if there is no join condition, I think join type doesn't matter and we can always push down limits. We may need to look into left anti join though.

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@guoxiaolongzte guoxiaolongzte Nov 28, 2018 •
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There are two tables as follows：
CREATE TABLE test1(id int, name int);
CREATE TABLE test2(id int, name int);

test1 table data:
2,2
1,1

test2 table data:
2,2
3,3
4,4

Execute sql select * from test1 t1 left anti join test2 t2 on t1.id=t2.id limit 1; The result:
1,1

But
we push the limit 1 on left side, the result is not correct. Result is empty.
we push the limit 1 on right side, the result is not correct. Result is empty.

So
left anti join no need to push down limit. Similarly, left semi join is the same logic.

case _ => join
}
LocalLimit(exp, newJoin)
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23 changes: 22 additions & 1 deletion sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LimitPushdownSuite.scala
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Expand Up @@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.analysis.EliminateSubqueryAliases
import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.expressions.Add
import org.apache.spark.sql.catalyst.plans.{FullOuter, LeftOuter, PlanTest, RightOuter}
import org.apache.spark.sql.catalyst.plans.{Cross, FullOuter, LeftOuter, PlanTest, RightOuter}
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules._

Expand Down Expand Up @@ -171,4 +171,25 @@ class LimitPushdownSuite extends PlanTest {
// No pushdown for FULL OUTER JOINS.
comparePlans(optimized, originalQuery)
}

test("cross join") {
val originalQuery = x.join(y, Cross).limit(1)
val optimized = Optimize.execute(originalQuery.analyze)
val correctAnswer = Limit(1, LocalLimit(1, x).join(LocalLimit(1, y), Cross)).analyze
comparePlans(optimized, correctAnswer)
}

test("cross join and left sides are limited") {
val originalQuery = x.limit(2).join(y, Cross).limit(1)
val optimized = Optimize.execute(originalQuery.analyze)
val correctAnswer = Limit(1, LocalLimit(1, x).join(LocalLimit(1, y), Cross)).analyze
comparePlans(optimized, correctAnswer)
}

test("cross join and right sides are limited") {
val originalQuery = x.join(y.limit(2), Cross).limit(1)
val optimized = Optimize.execute(originalQuery.analyze)
val correctAnswer = Limit(1, LocalLimit(1, x).join(LocalLimit(1, y), Cross)).analyze
comparePlans(optimized, correctAnswer)
}
}