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Fixing unexpected performance degradation caused by sub-optimal optimizer statistics

Oracle Database Tips by Donald Burleson

 

 

Question:  "My performance goes from good-to-bad as my data changes and my underlying execution plans change. How do I get a guaranteed optimal execution plan for my SQL?"

 

This is a very important and common question relating to performance instability that is the result of Oracle's semi-automated mechanisms to refresh the SQL optimizer statistics:

  • Dynamic sampling - When enabled, SQL may frequently change execution plans based on a quickie sample of the data distributions.
     

  • Automatic 10g statistics refreshing - The default value for refreshing statistics is

For any given SQL statement in your database, does there exist one, and only one, optimal execution plan? If you can generalize this for your whole database, then you are among the majority of shops with immutable access paths.  If so, you may want to consider archiving your optimal execution plans and use them forever, regardless of future changes to the data distribution.


More Details >> We are running a 10gR2 RAC database that is hosting OLTP application and we refresh our statistics on a daily basis. 

 

First, ask yourself; "Do I need to re-analyze my CBO statistics every day? Do my tables exceed the statistics refresh threshold (over 10% changed?) daily?  I am doing it as a "best practice?" Remember, the ONLY reason for analyzing your statistics is to CHANGE your SQL execution plans. What do I do to guarantee an always-optimal SQL execution plan for my whole database?"

 

Theoretically, any "lag" in the updating of CBO statistics may result in "stale" statistics and possible failure to "change the execution plan" to accommodate the chance to the data.  The classic example goes like this:

 

Consider an order processing table that is frequently accessed by a state_name column which contains 50 possible distinct values.  Depending on the specific value of the state_name (as specified in the SQL WHERE clause), the optimizer may need to frequently change the execution plan depending on the state_name value. 

 

  1. A popular value (select  . . WHERE state_name = California") might rightfully invoke a full-table scan
     

  2. A low popularity value (select . . . WHERE state_name = 'Idaho') would be better served with an index scan. 

In cases of reentrant SQL within the library cache (e.g. SQL with host variables), we have the "optimizer peeking" feature of cursor_sharing=similar.

 

More Details >>  The problem is that there are a few large, volatile tables (lots of inserts and updates).

Lots of inserts and updates are better than lots of DELETE's, which cause table and index fragmentation (i.e. logically deleted leaf nodes). Lots of INSERT/UPDATE will only extend the table in a uniform fashion.

More Details >> As the day progresses, the optimizer starts ignoring some of the indexes and forces full-table scans.

OK, what do we know for 100% certain? The execution plans changed, right?

So, what makes an execution plan change? Usually, defaultish features like Dynamic Sampling or not disabling the "automatic" statistics collection mechanism in 10g.

More Details >>  As a temporary work around, we refresh the stats on these specific tables throughout the day, but this is not really a solution we want to stick with due to the load that gathering the stats puts on the system.

Remember, it's perfectly acceptable to save and re-use your statistics. Lots of shops undertake to create a set of stats that work well for all SQL, save it, and rest-assured that the execution plans will not change. You can also export optimized statistics to your TEST and DEV instances, so that the developers will have their SQL optimized as-if it was on the PROD database.

For more details on saving your and re-using optimal statistics, see my OTN article on SQL Best Practices.

 

The nature of data distribution


There are two general types of Oracle shops. We have the 80% who have uniformly distributed data. Large tables remain large, and the distribution of values within a column remain unchanged.

On the other hand, we have roughly 20% of databases that experience highly volatile data loads, where tables are small on one day and huge the next, or cases where the is a "difference that makes a difference". In these databases, huge changes in the tables data (usually associated with high DML) changes the distribution of data values, necessitating a re-analysis of column histograms.

 

Histograms are critical to the CBO decision to choose an optimal table join order (hence the popularity of the ORDERED hint).

My advise, unless you are the rare database that does not have immutable execution plans, it a best practice to take a single, deep sample, determine the optimal placement of column histograms, and then save it as the "optimal" execution plan, once and forever.

 

It's the same concept as "optimizer plan stability" (stored outlines), which are used to firm-up execution plans during upgrades, where we want to test the database with our "old" execution plans before allowing the new release of Oracle to determine "new", and possibly sub-optimal execution plans.

 
 
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