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https://issues.apache.org/jira/browse/CALCITE-5213?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17877197#comment-17877197
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Ian Bertolacci edited comment on CALCITE-5213 at 8/27/24 10:14 PM:
-------------------------------------------------------------------
I figured out the discrepancy.
In our use of sqlToRelConverter, we apply the flattenTypes operation, after
which the correlation variables are no longer present.
In the tests, flattenTypes is only applied if withDecorrelate or withTrim are
set to true (which they are not).
We followed the sql processing pipeline from
[CalcitePrepareImpl.prepare_|https://github.com/apache/calcite/blob/main/core/src/main/java/org/apache/calcite/prepare/CalcitePrepareImpl.java#L1072-L1074].
I don't think thats important for us to use, so we will simply use the
unaltered RelRoot.
I'm not sure if flattenTypes' removing of the correlation variables is correct.
However, I'm having some issues with the nested correlated subquery that I
commented about previously.
When I use that same query, the inner projection does not have $cor1 in the
variablesSet. I don't know if I accidentally copied the expected values or if
something changed in my system to start giving wrong results?
In any-case, I do not get the correct RelNode tree from Sql to Rel conversion.
This is the result I get on both the 1.37 release and latest commit (787dfdb3)
{code}
LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
LogicalProject($f0=[+($5, $SCALAR_QUERY({
LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
LogicalProject(SAL=[$5])
LogicalFilter(condition=[=($3, $cor1.EMPNO)])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
}))])
LogicalFilter(condition=[=($3, $cor0.EMPNO)])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
})])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
{code}
was (Author: ian.bertolacci):
I figured out the discrepancy.
In our use of sqlToRelConverter, we apply the flattenTypes operation, after
which the correlation variables are no longer present.
In the tests, flattenTypes is only applied if withDecorrelate or withTrim are
set to true (which they are not).
We followed the sql processing pipeline from
[CalcitePrepareImpl.prepare_|https://github.com/apache/calcite/blob/main/core/src/main/java/org/apache/calcite/prepare/CalcitePrepareImpl.java#L1072-L1074].
I don't think thats important for us to use, so we will simply use the
unaltered RelRoot.
I'm not sure if flattenTypes' removing of the correlation variables is correct.
However, I'm having some issues with the nested correlated subquery that I
commented about previously.
When I use that same query, the inner projection does not have $cor1 in the
variablesSet. I don't know if I accidentally copied the expected values or if
something changed in my system to start giving wrong results?
In any-case, I do not get the correct RelNode tree from Sql to Rel conversion:
{code}
LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
LogicalProject($f0=[+($5, $SCALAR_QUERY({
LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
LogicalProject(SAL=[$5])
LogicalFilter(condition=[=($3, $cor1.EMPNO)])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
}))])
LogicalFilter(condition=[=($3, $cor0.EMPNO)])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
})])
LogicalTableScan(table=[[CATALOG, SALES, EMP]])
{code}
> PROJECT_TO_SUBQUERY producing Incorrect tree from nested correlated
> subqueries in projections with correlations in filters.
> ---------------------------------------------------------------------------------------------------------------------------
>
> Key: CALCITE-5213
> URL: https://issues.apache.org/jira/browse/CALCITE-5213
> Project: Calcite
> Issue Type: Bug
> Affects Versions: 1.30.0, 1.36.0, 1.37.0
> Reporter: Ian Bertolacci
> Priority: Major
>
> CoreRules.PROJECT_SUB_QUERY_TO_CORRELATE produces (what I believe to be)
> incorrect trees from nested correlated subqueries in projections.
> I'm hoping that I'm just doing something wrong and maybe someone will point
> it out.
> For example:
> {code:sql}
> SELECT (SELECT Sum(C202
> + (SELECT Sum(C101)
> FROM T1
> WHERE T1.ID = T2.C201))
> FROM T2
> WHERE T2.ID = T3.C302)
> FROM T3 {code}
> The initial RelNode tree produced from this SQL is:
> {code}
> 232:LogicalProject(EXPR$0=[
> | $SCALAR_QUERY({
> | LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
> | └──LogicalProject($f0=[+($2,
> | | $SCALAR_QUERY({
> | | LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
> | | └──LogicalProject(C101=[$1])
> | | └──LogicalFilter(condition=[=($0, $cor1.C201)])
> | | └──TableScan(table=[[QUERY, T1]], fields=[[ID, C101]])
> | | }))])
> | └──LogicalFilter(condition=[=($0, $cor0.C302)])
> | └──TableScan(table=[[QUERY, T2]], fields=[[ID, C201, C202, C203,
> C204]])
> | })])
> └──223:TableScan(table=[[QUERY, T3]], fields=[[ID, C301, C302]])
> {code}
> This looks ok so far, but it is important to notice the lack of variableSets
> in the projection nodes (which would appear in the filter nodes having
> correlated subqueries in their conditions).
> After applying the CoreRules.PROJECT_SUB_QUERY_TO_CORRELATE rule via a HEP
> program the resulting tree is:
> {code}
> 270:LogicalProject(EXPR$0=[$3])
> | // Unexpected Join instead of correlate binding $cor0
> └──268:LogicalJoin(condition=[true], joinType=[left])
> ├──246:TableScan(table=[[QUERY, T3]], fields=[[ID, C301, C302]])
> └──266:LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
> └──283:LogicalProject($f0=[+($2, $5)])
> | // Correlate node correctly binding $cor1
> └──281:LogicalCorrelate(correlation=[$cor1], joinType=[left],
> requiredColumns=[{1}])
> | // $cor0 is not bound by any parent correlate node
> ├──262:LogicalFilter(condition=[=($0, $cor0.C302)])
> | └──247:TableScan(table=[[QUERY, T2]], fields=[[ID, C201, C202,
> C203, C204]])
> └──279:LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
> └──277:LogicalProject(C101=[$1])
> | // $cor1 bound by #281
> └──275:LogicalFilter(condition=[=($0, $cor1.C201)])
> └──249:TableScan(table=[[QUERY, T1]], fields=[[ID,
> C101]])
> {code}
>
> Essentially, instead of a correlate node binding $cor0 there is a join (#268)
> and there is nothing binding $cor0.
> I would have expected this:
> {code}
> 270:LogicalProject(EXPR$0=[$3])
> | // Correlate binding $cor0 and requiring C302 from the LHS (#246)
> └──299:LogicalCorrelate(correlation=[$cor0], joinType=[left],
> requiredColumns=[{2}])
> ├──246:TableScan(table=[[QUERY, T3]], fields=[[ID, C301, C302]])
> └──266:LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
> └──283:LogicalProject($f0=[+($2, $5)])
> | // Correlate node correctly binding $cor1
> └──281:LogicalCorrelate(correlation=[$cor1], joinType=[left],
> requiredColumns=[{1}])
> | // $cor0 bound by #299
> ├──262:LogicalFilter(condition=[=($0, $cor0.C302)])
> | └──247:TableScan(table=[[QUERY, T2]], fields=[[ID, C201, C202,
> C203, C204]])
> └──279:LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
> └──277:LogicalProject(C101=[$1])
> | // $cor1 bound by #281
> └──275:LogicalFilter(condition=[=($0, $cor1.C201)])
> └──249:TableScan(table=[[QUERY, T1]], fields=[[ID,
> C101]])
> {code}
> Further, when adding CoreRules.JOIN_TO_CORRELATE in an attempt to convert the
> erroneous join into a correlation we get:
> {code}
> 322:LogicalProject(EXPR$0=[$3])
> | // Correlate incorrectly binding $cor2 (which does not appear anywhere)
> | // when it should be binding $cor0, and requiring no columns from the left
> side
> └──324:LogicalCorrelate(correlation=[$cor2], joinType=[left],
> requiredColumns=[{}])
> ├──298:TableScan(table=[[QUERY, T3]], fields=[[ID, C301, C302]])
> └──318:LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
> └──337:LogicalProject($f0=[+($2, $5)])
> | // Correlate node correctly binding $cor1
> └──335:LogicalCorrelate(correlation=[$cor1], joinType=[left],
> requiredColumns=[{1}])
> | // $cor0 is not bound by any parent correlate node
> ├──314:LogicalFilter(condition=[=($0, $cor0.C302)])
> | └──299:TableScan(table=[[QUERY, T2]], fields=[[ID, C201, C202,
> C203, C204]])
> └──333:LogicalAggregate(group=[{}], EXPR$0=[SUM($0)])
> └──331:LogicalProject(C101=[$1])
> | // $cor1 bound by #335
> └──329:LogicalFilter(condition=[=($0, $cor1.C201)])
> └──301:TableScan(table=[[QUERY, T1]], fields=[[ID,
> C101]])
> {code}
> which does replace the join with a correlate, but the correlate is incorrect;
> binding an undefined correlation variable and requiring no columns.
> Even further, (and this might be a separate issue all together), applying
> RelDecorrelator as an additional program in the sequence produces a very
> incorrect tree (both with and without the JOIN_TO_CORRELATE rule):
> {code}
> 1041:LogicalProject(EXPR$0=[$5], ID6=[$4])
> └──1039:LogicalJoin(condition=[true], joinType=[left])
> ├──1006:TableScan(table=[[QUERY, T3]], fields=[[ID, C301, C302, C303]])
> └──1037:LogicalAggregate(group=[{0}], EXPR$0=[SUM($1)])
> └──1035:LogicalProject(ID6=[$0], $f0=[+($2, $6)])
> └──1033:LogicalJoin(condition=[=($1, $5)], joinType=[left])
> ├──1024:LogicalFilter(condition=[=($0, $0)])
> | └──1007:TableScan(table=[[QUERY, T2]],
> | fields=[[ID, C201, C202, C203, C204]])
> └──1031:LogicalAggregate(group=[{0}], EXPR$0=[SUM($1)])
> └──1029:LogicalProject(ID=[$0], C101=[$1])
> └──1027:LogicalFilter(condition=[=($0, $0)])
> └──1009:TableScan(table=[[QUERY, T1]], fields=[[ID,
> C101]])
> {code}
> This tree now has more projection expressions than the original query, which
> is fully incorrect.
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