Ian Bertolacci created CALCITE-5213:
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Summary: 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
Reporter: Ian Bertolacci
One of the "standard" subquery to correlate rules
(CoreRules.PROJECT_SUB_QUERY_TO_CORRELATE,
CoreRules.FILTER_SUB_QUERY_TO_CORRELATE and
CoreRules.JOIN_SUB_QUERY_TO_CORRELATE), the resulting tree is:
{code}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:QueryTableScan(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:QueryTableScan(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:QueryTableScan(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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