The execution path for any SQL statement is only as good as the underlying statistics and it’s been long understood that histograms are the solution.

It’s very difficult for any SQL optimizer to accurately predict the cardinality of an operation, and the problem is aggravated by queries that have complex WHERE clause predicates.  The goal of any SQL optimizer is to join the tables together with the proper “driving table”, such that the first join has the smallest possible result set size (cardinality), resulting in less baggage that must be passed-on to subsequent table joins.

This is especially problematic for Oracle queries that join many tables together, and DBA’s now understand that optimal table join order is not automatic.  Instead, the DBA is forced to perform complex manual tuning, examining popular SQL statements and applying histograms as needed to ensure that the 11g CBO joins the tables together in an optimal fashion.  

In the real world, there exists one, and only one, optimal table join order, and rather than undertake a time-consuming exercise in histogram generation, they lock-down the table join orders with the ORDERED hint or by using SQL profiles.

Optimization requires historical SQL analysis

This issue is not unique to Oracle.  Anticipating the inter join cardinality is extremely complex, and the problem can only be solved by correlating the sub-optimal SQL to historical SQL statements:

- Histograms impose overhead – The presence of a histogram creates overhead for the CBO, and histograms cannot be applied solely by examining the table and index data.  Rather, histograms should only be created when SQL statements need them, and this requires complex analysis of historical SQL using STATSPACK or AWR tables.

- Intelligent histograms placement is time-consuming – Proper application of histograms requires careful analysis, and many production DBA’s do not have the time to correlate historical SQL with CBO statistics.

In an RMOUG 2008 paper titled “How Sampling Error Impacts Execution Plans: The Effects of Estimating Optimizer Statistics”, David Lipowitz, notes that histograms are critical to making the CBO choose an optimal table join order:

“The key lies in how the CBO calculates join cardinalities, which is Oracle's expectation of how many records will be produced when multiple tables are joined together.

The RDBMS assumes an even distribution of values in the absence of a histogram, and because of how we populated this table we know this is an accurate assumption.”

Lipowitz also notes that the CBO statistics collection mechanism does not yet examine SQL workloads, a critical factor that now must be done manually by the DBA:

“The optimizer's algorithm, using initialization parameters, system statistics, and most likely a variety of other inputs, essentially decides on a cardinality ahead of time that will allow the index-driven path to be used.

When that threshold is exceeded by a join cardinality based on the right combination of NUM_DISTINCT and NUM_ROWS, a full scan appears in the execution plan every time.

The probability of exceeding that threshold, given the specific statistics in the data dictionary, is all that varies; the threshold itself appears constant.”

Conclusions on CBO errors

Making the Oracle optimizer always choose the best execution plan is a phenomenal software engineering challenge, and the complex nature of the problem suggests that it may be impossible to ever create a SQL optimizer that never makes mistakes.  Until Oracle starts to leverage the historical data in the Automated Workload Repository (AWR), the CBO will never be able to properly add histograms. 

Far from the hype of 11g having “fully automated SQL tuning”, Oracle has a long way to go in automating the generation of the metadata statistics that are required to generate optimal execution plans for any SQL statement.  In the meantime, Oracle professionals will be forced to use techniques such as hints and adjusting optimizer parameters to overcome for this inherent problem. 

Despite the inherent complexity, the good news is that it is possible to intelligently examine your historical SQL and table column distributions and add histograms to improve execution plans.  Burleson Consulting has solved this problem and can add histograms to improve SQL execution plans.

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