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Don Burleson Blog 









Oracle dbms_stats tips

Oracle Database Tips by Donald Burleson

Prior to Oracle 10g, adjusting optimizer parameters was the only way to compensate for sample size issues with dbms_stats.  As of 10g, the use of dbms_stats.gather_system_stats and improved sampling within dbms_stats had made adjustments to these parameters far less important.  Ceteris Parabus, always adjust CBO statistics before adjusting optimizer parms.  For more details on optimizer parameters, see my latest book "Oracle Tuning: The Definitive Reference". 

As a review, the CBO gathers information from many sources, and he has the lofty goal of using DBA-provided metadata to always make the "best" execution plan decision:

Oracle uses data from many sources to make an execution plan

Let's examine the following areas of CBO statistics and see how to gather top-quality statistics for the CBO and how to create an appropriate CBO environment for your database.

Getting top-quality statistics for the CBO. The choices of executions plans made by the CBO are only as good as the statistics available to it. The old-fashioned analyze table and dbms_utility methods for generating CBO statistics are obsolete and somewhat dangerous to SQL performance. As we may know, the CBO uses object statistics to choose the best execution plan for all SQL statements.

The dbms_stats utility does a far better job in estimating statistics, especially for large partitioned tables, and the better statistics result in faster SQL execution plans. Here is a sample execution of dbms_stats with the OPTIONS clause:

exec dbms_stats.gather_schema_stats( - 
  ownname          => 'SCOTT', - 
  options          => 'GATHER AUTO', - 
  estimate_percent => dbms_stats.auto_sample_size, - 
  method_opt       => 'for all columns size repeat', - 
  degree           => 34 - 
Here is another dbms_stats example that creates histograms on all indexes columns:

There are several values for the OPTIONS parameter that we need to know about:

  • GATHER_ reanalyzes the whole schema
  • GATHER EMPTY_ only analyzes tables that have no existing statistics
  • GATHER STALE_ only reanalyzes tables with more than 10 percent modifications (inserts, updates,   deletes)
  • GATHER AUTO_ will reanalyze objects that currently have no statistics and objects with stale statistics.  Using GATHER AUTO is like combining GATHER STALE and GATHER EMPTY.

Note that both GATHER STALE and GATHER AUTO require monitoring. If you issue the ALTER TABLE XXX MONITORING command, Oracle tracks changed tables with the dba_tab_modifications view. Below we see that the exact number of inserts, updates and deletes are tracked since the last analysis of statistics:

SQL> desc dba_tab_modifications;

 Name                Type
 TABLE_NAME          VARCHAR2(30)
 INSERTS             NUMBER
 UPDATES             NUMBER
 DELETES             NUMBER
 TRUNCATED           VARCHAR2(3)

The most interesting of these options is the GATHER STALE option. Because all statistics will become stale   quickly in a robust OLTP database, we must remember the rule for GATHER STALE is > 10% row change   (based on num_rows at statistics collection time). Hence, almost every table except read-only tables will be reanalyzed with the GATHER STALE option, making the GATHER STALE option best for systems that are       largely read-only. For example, if only five percent of the database tables get significant updates, then only        five percent of the tables will be reanalyzed with the GATHER STALE option.

Automating sample size with dbms_stats.The better the quality of the statistics, the better the job that the    CBO will do when determining your execution plans. Unfortunately, doing a complete analysis on a large  database could take days, and most shops must sample your database to get CBO statistics. The goal is to take a large enough sample of the database to provide top-quality data for the CBO.

Now that we see how the dbms_stats option works, let's see how to specify an adequate sample size for dbms_stats.

In earlier releases, the DBA had to guess what percentage of the database provided the best sample size and sometimes under-analyzed the schema. Starting with Oracle9i Database, the estimate_percent argument is a great way to allow Oracle's dbms_stats to automatically estimate the "best" percentage of a segment to sample when gathering statistics:

estimate_percent => dbms_stats.auto_sample_size

Andrew Holdsworth of Oracle Corporation notes that dbms_stats is essential to good SQL performance, and it should always be used before adjusting any of the Oracle optimizer initialization parameters:

"The payback from good statistics management and execution plans will exceed any benefit of init.ora tuning by orders of magnitude"

Export Import statistics with dbms_stats

You can use the Oracle dbms_stats and export utilities to migrate schema statistics from your PROD instance to your TEST instance, so that your developers will be able to do more-realistic execution-plan tuning of new SQL before it's migrated into PROD.  Here are the steps:

Step 1: Create the stats_table:

exec dbms_stats.create_stat_table(ownname => 'SYS', stattab => 'prod_stats', - >
tblspace => 'SYSTEM');

Step 2: Gather the statistics with gather_system_stats.  In this dbms_stats example, we compute histograms on all indexed columns:


Step 3: Export the stats to the prod_stats table using export_system_stats::

exec dbms_stats.export_system_stats(ownname => 'SYS', stattab => 'prod_stats');

Step 4: Export the stats to the prod_stats table using exp:

exp scott/tiger file=prod_stats.dmp log=stats.log tables=prod_stats rows=yes

Step 5: FTP to the production server:

ftp -i prodserv . . .

Step 6: Import the stats from the prod_stats.dmp table using the import (imp) utility:

imp scott/tiger file=prod_stats.dmp log=stats.log tables=prod_stats rows=yes

Step 7: We can now use the import_system_stats procedure in Oracle dbms_stats to overlay the existing CBO statistics from the smaller TEST instance:


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

For more details, see these notes on 11g extended optimizer statistics.

This advice from the Oracle Real-world tuning group:

>> 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.

>> 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.

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See my related dbms_stats notes:




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