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Oracle SQL Join Cardinality

Oracle Tips by Burleson Consulting

How is join cardinality estimated?

In the absence of column histograms, the Oracle CBO must be able to guess on information, and sometimes the guess is wrong. This is one reason why the ORDERED hint is one of the most popular SQL tuning hints, because using the ORDERED hint allows the DBA to specify that the tables be joined together in the same order that they appear in the FROM clause, like this:

select /+ ORDERED */

student_name

from

student

natural join

course

natural join

professor

where

professor = ‘jones’

and

course = ‘anthropology 610’;

If the values for the PROFESSOR and COURSE table columns are not skewed, it is unlikely that the 10g automatic statistics would have created histograms buckets in the dba_histograms view for these columns.

The Oracle CBO needs to be able to accurately estimate the final number of rows returned by each step of the query and then use schema metadata from running dbms_stats to choose the table join order that results in the least amount of baggage, in the form of intermediate rows, from each of the table join operations.

This is a daunting task. When an SQL query has a complex WHERE clause, it can be very difficult to estimate the size of the intermediate result sets, especially when the WHERE clause transforms column values with mathematical functions. Oracle has made a commitment to making the CBO infallible, even when incomplete information exists.

Oracle9i introduced the new dynamic sampling method for gathering run-time schema statistics, and it is now enabled by default in Oracle10g.

It's well established that histograms are very useful for helping the optimizer choose between a full-scan and index-scan, and we also see evidence that histograms help Oracle figure out inter-table join cardinality (the amount of "baggage" that Oracle must carry between table joins). The presence of histograms helps the CBO determine the optimal table join order.

Oracle expert David Aldridge just published an outstand treatise titled "Histograms for join predicates" where he does an outstanding job explaining the problem and how to use histograms as a solution. The thrust of the argument is that histograms will help detect "skew" in table join columns (STATE_NAME) when using synthetic keys (STATE#). Aldridge shares his conclusions:

To sum up, here are the solutions I thought of:

Partition or subpartition the fact table on STATE#. (preferred option)

Create a summary table with partitioning or subpartitoning on STATE#. (uses most space and slows data load, but very flexible and powerful)

Create a function-based index on fact to perform the lookup, and query that value instead. (a bit flaky, but it works without major system impact)

Rebuild the fact table based on the STATE_NAME instead. (still limited in multi-level hierarchies)

SEE CODE DEPOT FOR FULL SCRIPTS