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https://issues.apache.org/jira/browse/CALCITE-1288?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Gautam Kumar Parai updated CALCITE-1288:
----------------------------------------
Description:
When the query has one distinct aggregate and one or more non-distinct
aggregates, the join instance need not produce the join-based plan. We can
generate multi-phase aggregates. Another approach would be to use grouping
sets. However, this transformation will be useful when systems don't support
grouping sets and instead rely on the join-based plans (see the plan below)
{code}
select emp.empno, count(*), avg(distinct dept.deptno)
from sales.emp emp inner join sales.dept dept
on emp.deptno = dept.deptno
group by emp.empno
LogicalProject(EMPNO=[$0], EXPR$1=[$1], EXPR$2=[$3])
LogicalJoin(condition=[IS NOT DISTINCT FROM($0, $2)], joinType=[inner])
LogicalAggregate(group=[{0}], EXPR$1=[COUNT()])
LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
LogicalJoin(condition=[=($7, $9)], joinType=[inner])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
LogicalAggregate(group=[{0}], EXPR$2=[AVG($1)])
LogicalAggregate(group=[{0, 1}])
LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
LogicalJoin(condition=[=($7, $9)], joinType=[inner])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
{code}
The more efficient form should look like
{code}
select emp.empno, count(*), avg(distinct dept.deptno)
from sales.emp emp inner join sales.dept dept
on emp.deptno = dept.deptno
group by emp.empno
LogicalAggregate(group=[{0}], EXPR$1=[SUM($2)], EXPR$2=[AVG($1)])
LogicalAggregate(group=[{0, 1}], EXPR$1=[COUNT()])
LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
LogicalJoin(condition=[=($7, $9)], joinType=[inner])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
{code}
was:
When the query has one distinct aggregate and one or more non-distinct
aggregates, the join instance need not produce the join-based plan. We can
generate multi-phase aggregates.
{code}
select emp.empno, count(*), avg(distinct dept.deptno)
from sales.emp emp inner join sales.dept dept
on emp.deptno = dept.deptno
group by emp.empno
LogicalProject(EMPNO=[$0], EXPR$1=[$1], EXPR$2=[$3])
LogicalJoin(condition=[IS NOT DISTINCT FROM($0, $2)], joinType=[inner])
LogicalAggregate(group=[{0}], EXPR$1=[COUNT()])
LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
LogicalJoin(condition=[=($7, $9)], joinType=[inner])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
LogicalAggregate(group=[{0}], EXPR$2=[AVG($1)])
LogicalAggregate(group=[{0, 1}])
LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
LogicalJoin(condition=[=($7, $9)], joinType=[inner])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
{code}
The more efficient form should look like
{code}
select emp.empno, count(*), avg(distinct dept.deptno)
from sales.emp emp inner join sales.dept dept
on emp.deptno = dept.deptno
group by emp.empno
LogicalAggregate(group=[{0}], EXPR$1=[SUM($2)], EXPR$2=[AVG($1)])
LogicalAggregate(group=[{0, 1}], EXPR$1=[COUNT()])
LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
LogicalJoin(condition=[=($7, $9)], joinType=[inner])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
{code}
> Avoid doing the same join twice if count(distinct) exists
> ---------------------------------------------------------
>
> Key: CALCITE-1288
> URL: https://issues.apache.org/jira/browse/CALCITE-1288
> Project: Calcite
> Issue Type: Improvement
> Reporter: Gautam Kumar Parai
> Assignee: Gautam Kumar Parai
>
> When the query has one distinct aggregate and one or more non-distinct
> aggregates, the join instance need not produce the join-based plan. We can
> generate multi-phase aggregates. Another approach would be to use grouping
> sets. However, this transformation will be useful when systems don't support
> grouping sets and instead rely on the join-based plans (see the plan below)
> {code}
> select emp.empno, count(*), avg(distinct dept.deptno)
> from sales.emp emp inner join sales.dept dept
> on emp.deptno = dept.deptno
> group by emp.empno
> LogicalProject(EMPNO=[$0], EXPR$1=[$1], EXPR$2=[$3])
> LogicalJoin(condition=[IS NOT DISTINCT FROM($0, $2)], joinType=[inner])
> LogicalAggregate(group=[{0}], EXPR$1=[COUNT()])
> LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
> LogicalJoin(condition=[=($7, $9)], joinType=[inner])
> LogicalTableScan(table=[[CATALOG, SALES, EMP]])
> LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
> LogicalAggregate(group=[{0}], EXPR$2=[AVG($1)])
> LogicalAggregate(group=[{0, 1}])
> LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
> LogicalJoin(condition=[=($7, $9)], joinType=[inner])
> LogicalTableScan(table=[[CATALOG, SALES, EMP]])
> LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
> {code}
> The more efficient form should look like
> {code}
> select emp.empno, count(*), avg(distinct dept.deptno)
> from sales.emp emp inner join sales.dept dept
> on emp.deptno = dept.deptno
> group by emp.empno
> LogicalAggregate(group=[{0}], EXPR$1=[SUM($2)], EXPR$2=[AVG($1)])
> LogicalAggregate(group=[{0, 1}], EXPR$1=[COUNT()])
> LogicalProject(EMPNO=[$0], DEPTNO0=[$9])
> LogicalJoin(condition=[=($7, $9)], joinType=[inner])
> LogicalTableScan(table=[[CATALOG, SALES, EMP]])
> LogicalTableScan(table=[[CATALOG, SALES, DEPT]])
> {code}
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