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Oracle optimizer extended statistics tips

Oracle extended statistics Tips by Burleson Consulting

December 13, 2007, Updated February 27th, 2015

One of the most exciting new features of Oracle 11g is improvements to the dbms_stats package, specifically the ability to aid complex queries by providing extended statistics to the cost-based optimizer (CBO).

There is a hidden parameter _optimizer_enable_extended_stats = true that enables extended statistics.

For details on creating optimizer extended statistics, see these important notes on dbms_stats.create_extended_stats.

The 11g extended statistics are intended to improve the optimizers guesses for the cardinality of combined columns and columns that are modified by a built-in or user-defined function.

In Oracle 10g we saw how that dynamic sampling can be used to provide inter-table cardinality estimates, but dynamic sampling has important limitations.  However, the 11g create_extended_stats in dbms_stats relieves much of the problem of sub-optimal table join orders allowing for extended statistics on correlated columns.

In the absence of column histograms and extended statistics, the Oracle cost-based optimizer must be able to ?guess? the size of complex result sets information, and it sometimes gets it wrong. This is one reason why the ORDERED hint is one of the most popular SQL tuning hints; using the ORDERED hint allows you to specify that the tables be joined together in the same order that they appear in the FROM clause.

In this example, the four-way table join only returns 18 rows, but the query carries 9,000 rows in intermediate result sets, slowing-down the SQL execution speed:

A suboptimal table join order

If we were able to better predict the sizes of the intermediate results by tipping-off the optimizer with extended statistics, we can re-sequence the table-join order to carry less ?intermediate baggage? during the four-way table join, in this example carrying only 3,000 intermediate rows between the table joins:

11g extended statistics help the CBO predict inter-table join result set sizes

Extended statistics supplemental notes

Oracle guru Guy Harrison also offers this advice for advanced statistics collection.

"In 11g, I think there are two other ways to get statistics collected for indexed expressions:

1) Collect extended statistics directly on the expression. So for instance, if we had a function SALES_CATEGORY, we might do this:"

   (ownname => USER,

    tabname => ?SALES?,
    method_opt => ?FOR ALL COLUMNS FOR COLUMNS
   (sale_category(amount_sold))? );

2) Create a virtual column on the expression, then index that column. So for the same example as above we might create the following virtual column, then index the column and collect stats as usual:


"I think I like the first method better, because the statistics will still exist even if the index is dropped and ? unlike the second approach ? it doesn?t change the logical structure of the table.

Arup Nanda has a great article on extended statistics with dbms_stats, specialty histogram analysis using function-based columnar data:

"Next, re-gather statistics on the table and collect the extended statistics on the expression upper(cust_name)."

  dbms_stats.gather_table_stats (
     ownname    => 'ARUP',
     tabname    => 'CUSTOMERS',
     method_opt => 'for all columns size skewonly for columns 

Alternatively you can define the column group as part of the gather statistics command. You do that by placing these columns in the method_opt parameter of the gather_table_stats procedure in dbms_stats as shown below:

  dbms_stats.gather_table_stats (
   ownname         => 'ARUP',
   tabname         => 'BOOKINGS',
   estimate_percent=> 100,
   cascade         => true


This page is related to the book "Oracle 11g New Features" by Rampant TechPress.


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