[jira] Resolved: (DERBY-634) Subquery materialization can cause stack overflow

["Satheesh Bandaram (JIRA)" <de...@db.apache.org>]

     [ http://issues.apache.org/jira/browse/DERBY-634?page=all ]

Satheesh Bandaram resolved DERBY-634.
-------------------------------------

    Fix Version/s: 10.3.0.0
                       (was: 10.2.1.0)
       Resolution: Fixed

I would like this fix backported to 10.2 release as soon as possible. Have a customer that is waiting for this fix for a long time.


> Subquery materialization can cause stack overflow
> -------------------------------------------------
>
>                 Key: DERBY-634
>                 URL: http://issues.apache.org/jira/browse/DERBY-634
>             Project: Derby
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 10.1.1.1
>            Reporter: Jeff Lichtman
>             Fix For: 10.3.0.0
>
>         Attachments: DERBY-634.diff
>
>
> A performance optimization in subquery processing can cause a stack overflow.
> The optimization materializes a subquery ResultSet in memory where it thinks the rows will fit in memory. The materialization is done as a  set of  nested unions of constant rows (UnionResultSets and RowResultSets). If there are a lot of rows this can cause a stack overflow when fetching a row.
> The obvious fix is to make it use an iterative technique rather than a recursive one for storing and returning the rows. See the method BaseActivation.materializeResultSetIfPossible() in the language execution code.
> There are some other issues with this performance optimization that should be looked at:
> 1) The optimization can backfire, making the query run much slower. For example, in the query:
>     select * from one_row_table where column1 not in
>         (select column2 from million_row_table)
> reading million_row_table into memory is an expensive operation. If there is an index on million_row_table.column2, the query should return a result very quickly despite the large size of million_row_table by doing a single probe into million_row_table via the index.
> Since in-memory materialization can be an expensive operation, the decision about whether to do it should be made based on query optimizer cost estimates. See SubqueryNode.generateExpression().
> 2) It may not be wise to cache partial query results in memory at all. Although this can help performance in some cases, it also chews up memory. This is different from a limited-size cache with a backing store (like what the store uses for page caching). The language has no way to limit the total amount of memory used in this type of processing. Note that hash joins originally used in-memory hash tables with no backing store, and that a backing store was added later.
> 3) The implementation of this optimization has some problems. The decision to materialize the subquery results in memory is made during code generation - all such decisions should be made during the compilation phase. It's not clear to me why materializeResultSetIfPossible() is in BaseActivation - I would expect the of materialization to be done by a type of ResultSet, not by a method in BaseActivation. Also, this method calls getMaxMemoryPerTable() in the OptimizerFactory - nothing in the execution code should refer to anything in the compilation code (perhaps getMaxMemoryPerTable() should be moved somewhere else).

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