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Bloom Filter Efficiency And Cardinality Estimates

I've recently came across an interesting observation I've not seen documented yet, so I'm publishing a simple example here to demonstrate the issue.

In principle it looks like that the efficiency of Bloom Filter operations are dependent on the cardinality estimates. This means that in particular cardinality under-estimates of the optimizer can make a dramatic difference how efficient a corresponding Bloom Filter operation based on such a cardinality estimate will work at runtime. Since Bloom Filters are crucial for efficient processing in particular when using Exadata or In Memory column store this can have significant impact on the performance of affected operations.

12c Adaptive Joins Plus Statistics Feedback For Joins Cardinality Estimate Bug

I've encountered a bug at several clients that upgraded to Oracle 12c - - that requires the combination of several new adaptive features introduced with Oracle 12c.It needs an execution plan that makes use of adaptive join methods, plus at runtime the activation of the new "statistics feedback for joins" feature that was also introduced with Oracle 12c. Note that in 11.2 there was already the "cardinality feedback" feature that only applies to single table cardinality misestimates, but not to join cardinality misestimates.In case then the join method used at runtime is a Nested Loop join - not necessarily the join method preferred initially, so a runtime switch from Hash to Nested Loop join also reproduces the problem - the "statistics feedback for joins" feature generates a bad OPT_ESTIMATE hint for the join cardinality that always seems to be one, like the following: OPT_ESTIMATE(...