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method_opt tips

Oracle Tips by Burleson Consulting

Updated January 25, 2015

Question:  How does the method_opt part of dbms_stats work to generate histograms and how do I use method_opt in a typical database?  I only want to generate histograms when they are needed.  What are the method_opt best practices?

Answer:  The method_opt parameter is used to control the generation of histograms.  See my research on understanding histograms and  tuning with histograms.

Important:  Whenever you have almost all of your SQL using bind variables, you will want to delete your existing table statistics and then change the method_opt to prevent future histogram creation.  To delete the histograms that were created with method_opt, just remove and re-analyze the tables using dbms_stats,delete_table_stats.  To prevent future generation of histograms, invoke dbms_stats.set_param as follows

exec dbms_stats.set_param(pname=>'METHOD_OPT', pval=>'FOR ALL COLUMNS SIZE 1');

The purpose of method_opt

The method_opt argument within dbms_stats controls the following:

  • The generation of histograms

  • The creation of extended statistics  (Oracle 11g)

  • The collection of "base" column statistics

Let's look at some of the many permutations of the method_opt clause.


The default method_opt "For all columns size auto"


Remember, it is wasteful to create a histogram on a column that is never referenced by a SQL statement.  It is the SQL workload that determines if a column needs histograms.

The default value of  "for all columns size auto" is the Oracle default and this option will analyze histograms on what the optimizer considers to be "important columns".  The optimizer does this by examining your current workload when you run the dbms_stats command, interrogating all current SQL in the library cache to see which columns might benefit from a histogram.

The method "for all indexed columns"

 The method "for all indexed columns" limits base column gathering to only those columns that are included in an index. This value is not recommended as it is highly unlikely that only index columns will be used in the select list, where clause predicates, and group by clause of all of the SQL statement executed in the environment.

Defining extended statistics with method_opt


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?,


One exciting feature of dbms_stats is the ability to automatically look for columns that should have histograms, and create the histograms.  Multi-bucket histograms add a huge parsing overhead to SQL statements, and histograms should ONLY be used when the SQL will choose a different execution plan based upon the column value.

To aid in intelligent histogram generation, Oracle uses the method_opt parameter of dbms_stats.  There are also important new options within the method_opt clause, namely skewonly, repeat and auto.

      method_opt=>'for all columns size skewonly'
      method_opt=>'for all columns size repeat'
      method_opt=>'for all columns size auto'  -- The default in 10g and beyond

In practice, there is a specific order to use the different options of dbms_stats.  See this article for details.  Let?s take a close look at each method option.


The method_opt='SKEWONLY'  dbms_stats Option

The first is the 'skewonly' option which very time-intensive because it examines the distribution of values for every column within every index. 

If dbms_stats discovers an index whose columns are unevenly distributed, it will create histograms for that index to aid the cost-based SQL optimizer in making a decision about index vs. full-table scan access. 

For example, if an index has one column that is in 50% of the rows, a full-table scan is faster than and index scan to retrieve these rows.

Histograms are also used with SQL that has bind variables and SQL with cursor_sharing enabled.  In these cases, the CBO determines if the column value could affect the execution plan, and if so, replaced the bind variable with a literal and performs a hard parse.


-- SKEWONLY option : Detailed analysis
-- Use this method for a first-time analysis for skewed indexes
-- This runs a long time because all indexes are examined
      ownname          => 'SCOTT',
      estimate_percent => dbms_stats.auto_sample_size,
      method_opt       => 'for all columns size skewonly',
      degree           => 7


The method_opt='REPEAT'  dbms_stats Option

Following the one-time detailed analysis, the re-analyze task will be less resource intensive with the REPEAT option.  Using the repeat option will only re-analyze indexes with existing histograms, and will not search for other histograms opportunities.  This is the way that you will re-analyze you statistics on a regular basis.

-- REPEAT OPTION - Only re-analyze histograms for indexes
-- that have histograms
-- Following the initial analysis, the weekly analysis
-- job will use the ?repeat? option.  The repeat option
-- tells dbms_stats that no indexes have changed, and
-- it will only re-analyze histograms for
-- indexes that have histograms.
      ownname          => 'SCOTT',
      estimate_percent => dbms_stats.auto_sample_size,
      method_opt       => 'for all columns size repeat',
      degree           => 7


The method_opt='AUTO' dbms_stats Option

The auto option is used when monitoring is implemented and creates histograms based upon data distribution and the manner in which the column is accessed by the application (e.g. the workload on the column as determined by monitoring, especially foreign keys to determine the cardinality of table join result sets).  Using method_opt=>?auto? is similar to using the gather auto in the option parameter of dbms_stats. 

      ownname          => 'SCOTT',
      estimate_percent => dbms_stats.auto_sample_size,
      method_opt       => 'for all columns size auto',
      degree           => 7
Remember, analyzing for histograms is time-consuming, and histograms are used under two conditions:
  • Table join order : The CBO must know the size of the intermediate result sets (cardinality) to properly determine the correct join order the multi-table joins.
  • Table access method : The CBO needs to know about columns in SQL where clauses, where the column value is skewed such that a full-table scan might be faster than an index range scan. Oracle uses this skew information in conjunction with the clustering_factor columns of the dba_indexes view.
Hence, this is the proper order for using the dbms_stats package to locate proper columns for histograms:
1. Skewonly option - You want to use skewonly to do histograms for skewed columns, for cases where the value will make a difference between a full-table scan and an index scan.

2. Monitor - Next, turn-on monitoring. Issue an ?alter table xx monitoring? and ?alter index yyy monitoring? command for all segments in your schema. This will monitor workload against

3. Auto option - Once monitoring is in-place, you need to re-analyze with the "auto" option to create histograms for join columns within tables. This is critical for the CBO to determine the proper join order for finding the driving table in multi-table joins.

4. Repeat option - Finally, use the "repeat" option to re-analyze only the existing histograms.
Periodically you will want to re-run the skewonly and auto option to identify any new columns that require histograms. Once located, the repeat option will ensure that they are refreshed with current values.

In Oracle we now see 11g extended optimizer statistics, an alternative to dynamic_sampling for estimating result set sizes.

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 (upper(cust_name))'

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

Usage notes for method_opt:

This advice comes from Greg Rahn of the Oracle Real-world tuning group:

Question:  Will dbms_stats someday detect sub-optimal table join orders from a workload, and create appropriate histograms?

If histograms exist, then they were either automatically created because the columns met the criteria defined on page 10-11 of the document, or manually created. If they were created automatically, then is probable they will influence the plan for the better.

Sub-optimal join orders are generally the result of poor cardinality estimates. Histograms are designed to help with cardinality estimates where data skew exists.

Question:  Keeping statistics:  What is the current "official" policy regarding statistics retention?

The old CW was that the DBA should collect a deep, representative sample, and keep it, only re-analyzing when it's "a difference that makes a difference"?

I don't know if there is an "official" policy per se, but I will offer my professional opinion based on experience. Start with the dbms_stats defaults. Modify as necessary based on plan performance. Use dynamic sampling and/or dbms_sqltune or hints/outlines where appropriate (probably in that order). Understand the problem before attempting solutions.

There are a couple of cases that I would be mindful of:

1) Experience has shown that poor plans can be a result of under estimated NDV with skewed data and DBMS_STATS.AUTO_SAMPLE_SIZE (or too small of a sample). This has been addressed/enhanced in 11g. In 10g it requires choosing a fixed sample size that yields an accurate enough NDV to get the optimal plan(s). The sample size will vary case by case as it is data dependent.

2) Low/High value issues on recently populated data, specifically with partitioned tables. If the partition granule size is small (say daily or smaller) the default 10% stale might be too little. It may be best to gather partition stats immediately after loading, or set them manually. It's better to have stats that are an over estimate on the number of rows/values than an under estimate. For example, its better to have a hash join on a small set of data than a nested loops on a large set.


Oracle 11g Updates:  In Oracle we now see 11g extended optimizer statistics, an alternative to dynamic_sampling for estimating result set sizes.  Also, Oracle guru Guy Harrison also offers this advice for 11g statistics collection on function-based index columns.  

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.


Also see:

Oracle speaks on 10g migration tips

Oracle CBO optimizer statistics dbms_stats histograms




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