Cardinality Feedback

Cardinality feedback was introduced in Oracle Database 11gR2. The purpose of this feature is to automatically improve plans for queries that are executed repeatedly, for which the optimizer does not estimate cardinalities in the plan properly. The optimizer may misestimate cardinalities for a variety of reasons, such as missing or inaccurate statistics, or complex predicates. Whatever the reason for the misestimate, cardinality feedback may be able to help.

During the first execution of a SQL statement, an execution plan is generated as usual. During optimization, certain types of estimates that are known to be of low quality (for example, estimates for tables which lack statistics or tables with complex predicates) are noted, and monitoring is enabled for the cursor that is produced. If cardinality feedback monitoring is enabled for a cursor, then at the end of execution, some of the cardinality estimates in the plan are compared to the actual cardinalities seen during execution. If some of these estimates are found to differ significantly from the actual cardinalities, the correct estimates are stored for later use. The next time the query is executed, it will be optimized again, and this time the optimizer uses the corrected estimates in place of its usual estimates.

Consider the following query, using the Oracle sample schema OE:

select product_name
from order_items o, product_information p
where o.unit_price = 15 and quantity > 1
and p.product_id = o.product_id; 

The combination of an equality filter predicate and an inequality filter predicate causes a misestimation in the cardinality of the order_items table after these filters are applied. The optimizer chooses the following plan:

We can see from the execution statistics (A-Rows column in the plan) that the cardinality of order_items is underestimated. On the next execution, the actual cardinality is fed back to the optimizer, and this time the optimizer gets the cardinality estimate right, and chooses a different plan:

We can tell that cardinality feedback was used because it appears in the note section of the plan. Note that you can also determine this by checking the USE_FEEDBACK_STATS column in V$SQL_SHARED_CURSOR.

Cardinality feedback monitoring may be enabled in the following cases: tables with no statistics, multiple conjunctive or disjunctive filter predicates on a table, and predicates containing complex operators that the optimizer cannot accurately compute selectivity estimates for.  In some cases, there are other techniques available to improve estimation; for instance, dynamic sampling or multi-column statistics allow the optimizer to more accurately estimate selectivity of conjunctive predicates.  In cases where these techniques apply, cardinality feedback is not enabled.  However, if multi-column statistics are not present for the relevant combination of columns, the optimizer can fall back on cardinality feedback.

Cardinality feedback is useful for queries where the data volume being processed is stable over time.  For a query on volatile tables, the first execution statistics are not necessarily reliable.  This feature is not meant to evolve plans over time as the data in the table changes; it is meant to address queries where the plan is not correct to begin with.  For similar reasons, execution statistics for queries containing bind variables can be problematic.  In cardinality feedback, we limit the feedback to portions of the plan whose estimates are not affected by bind variables.  For instance, if a table has a filter comparing a column to a bind value, the cardinality of that table will not be used.  However, cardinality feedback can still be used for cardinalities elsewhere in the plan.

The monitoring for cardinality feedback is disabled after the first execution.  Since the aim of cardinality feedback is to improve plans that should be stable over time, there is no benefit to continuous monitoring. Disabling monitoring minimizes the impact of cardinality feedback on queries where it is not necessary – for queries where the estimates are correct, this one time overhead is similar to the one time overhead of optimizing a query.

In Oracle Database 11gR2, cardinality feedback monitors and feeds back the following kinds of cardinalities:

  • Single table cardinality (after filter predicates are applied)
  • Index cardinality (after index filters are applied)
  • Cardinality produced by a group by or distinct operator

The cardinalities that can be observed during an execution depend on the shape of a plan.  So it is possible that on the second execution of a query, after generating a new plan using cardinality feedback, there are still more cardinality estimates that are found to deviate significantly from the actual cardinalities.  In this case, we can reoptimize yet again on the next execution.  However, we have safeguards in place to guarantee that this will stabilize after a small number of executions.  So you may see your plan changing in the first few executions, but then we eventually pick one and stick with it on all subsequent executions.

The improved estimates used by cardinality feedback are not persisted. For this reason, it’s always preferable to use other techniques to get cardinality estimates right the first time every time, e.g. extended statistics, dynamic sampling, or SQL profiles. But for cases where these techniques do not apply, cardinality feedback can provide some relief.

We discussed cardinality feedback in more detail, including a performance study of the feature, in our 2008 VLDB paper Closing the Query Processing Loop in Oracle 11g.

Comments:

> In cases where these techniques apply, cardinality feedback is not enabled

I see execution plans in 11.2.0.3 where both dynamic sampling and cardinality feedback are being used. Should this be happening?

Posted by Dominic Brooks on May 18, 2012 at 06:11 AM PDT #

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The Oracle Optimizer blog is written by members of the Optimizer development team. The goal of this blog is to provide an insight into the workings of the Optimizer and the statistics it relies on. The views expressed on this blog are our own and do not necessarily reflect the views of Oracle and its affiliates. The views and opinions expressed by visitors on this blog are theirs solely and may not reflect ours.

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