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https://issues.apache.org/jira/browse/CALCITE-6893?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17936109#comment-17936109
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Zhen Chen commented on CALCITE-6893:
------------------------------------
[~julianhyde] You are right. We can change the sql to
{code:java}
select job
from (
select job, 0 as i from "scott".emp where deptno = 10
union all
select job, 1 as i from "scott".emp where deptno = 20)
group by job
having count(distinct i) = 2; {code}
WDYT?
> Remove agg from Union children in IntersectToDistinctRule
> ---------------------------------------------------------
>
> Key: CALCITE-6893
> URL: https://issues.apache.org/jira/browse/CALCITE-6893
> Project: Calcite
> Issue Type: Improvement
> Reporter: Zhen Chen
> Assignee: Zhen Chen
> Priority: Major
> Labels: pull-request-available
> Attachments: image-2025-03-16-11-02-04-725.png
>
>
> Whether agg should be pushed down should be determined by the cost.
> SQL:
>
> {code:java}
> select ename from emp where deptno = 10
> intersect
> select ename from emp where deptno = 20{code}
>
> Then used rule INTERSECT_TO_DISTINCT(updated version) and
> AGGREGATE_UNION_TRANSPOSE in hep planner.
> We can get logical plan:
>
> {code:java}
> LogicalProject(ENAME=[$0])
> LogicalFilter(condition=[=($1, 2)])
> LogicalAggregate(group=[{0}], agg#0=[$SUM0($1)])
> LogicalUnion(all=[true])
> LogicalAggregate(group=[{0}], agg#0=[COUNT()])
> LogicalProject(ENAME=[$1])
> LogicalFilter(condition=[=($7, 10)])
> LogicalTableScan(table=[[CATALOG, SALES, EMP]])
> LogicalAggregate(group=[{0}], agg#0=[COUNT()])
> LogicalProject(ENAME=[$1])
> LogicalFilter(condition=[=($7, 20)])
> LogicalTableScan(table=[[CATALOG, SALES, EMP]]) {code}
> Then we also use the two same rules in volcanol planner.
>
> Final Phy Plan:
>
> {code:java}
> EnumerableProject(ENAME=[$0]): rowcount = 1.0, cumulative cost =
> {43.72500000000001 rows, 68.4 cpu, 0.0 io}, id = 85
> EnumerableFilter(condition=[=($1, 2)]): rowcount = 1.0, cumulative cost =
> {42.72500000000001 rows, 67.4 cpu, 0.0 io}, id = 84
> EnumerableAggregate(group=[{0}], agg#0=[COUNT()]): rowcount = 1.0,
> cumulative cost = {41.72500000000001 rows, 66.4 cpu, 0.0 io}, id = 83
> EnumerableUnion(all=[true]): rowcount = 4.2, cumulative cost =
> {40.60000000000001 rows, 66.4 cpu, 0.0 io}, id = 82
> EnumerableProject(ENAME=[$1]): rowcount = 2.1, cumulative cost =
> {18.200000000000003 rows, 31.1 cpu, 0.0 io}, id = 79
> EnumerableFilter(condition=[=($7, 10)]): rowcount = 2.1, cumulative
> cost = {16.1 rows, 29.0 cpu, 0.0 io}, id = 78
> EnumerableTableScan(table=[[CATALOG, SALES, EMP]]): rowcount =
> 14.0, cumulative cost = {14.0 rows, 15.0 cpu, 0.0 io}, id = 69
> EnumerableProject(ENAME=[$1]): rowcount = 2.1, cumulative cost =
> {18.200000000000003 rows, 31.1 cpu, 0.0 io}, id = 81
> EnumerableFilter(condition=[=($7, 20)]): rowcount = 2.1, cumulative
> cost = {16.1 rows, 29.0 cpu, 0.0 io}, id = 80
> EnumerableTableScan(table=[[CATALOG, SALES, EMP]]): rowcount =
> 14.0, cumulative cost = {14.0 rows, 15.0 cpu, 0.0 io}, id = 69
> {code}
>
> We can see the best plan, the children of union do not have agg.
> DAG:
> !image-2025-03-16-11-02-04-725.png|width=545,height=348!
> Currently, Calcite does not support distributed planning. If in a distributed
> planning, agg will be divided into two stages. If the filtering effect in the
> first stage is very good, the downward push of agg will be meaningful and
> reduce the network transmission of shuffle. However, optimizing the current
> rule is also meaningful. Calcite now also has rules that can do the downward
> push of agg. We can give the choice to the volcano.
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