[jira] [Created] (CALCITE-5213) PROJECT_TO_SUBQUERY producing Incorrect tree from nested correlated subqueries in projections with correlations in filters.

["Ian Bertolacci (Jira)" <ji...@apache.org>]

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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