Rethinking Oracle Optimizer Statistics for P6 Part 2

In the previous post (Part 1), I tried to draw some key insights about the relationship between P6 and Oracle Optimizer Statistics.  The first is that average cardinality has the greatest impact on query optimization and that the particular queries generated by P6 are more likely to use this average during calculations. The second is that these are statistics that are unlikely to change greatly over the life of the application. Ultimately, our goal is to get the best query optimization possible.  Or is it?

Stability

No application administrator wants to get the call at 9am that their application users cannot get there work done because everything is running slow. This is a possibility with a regularly scheduled nightly collection of statistics. It may not just be slow performance, but a complete loss of service because one or more queries are optimized poorly. Ideally, this should not be the case. The database optimizer should make better decisions with more up-to-date data. Better statistics may give incremental performance benefit. However, this benefit must be balanced against the potential cost of system down time. 

It is stability that we ultimately desire and not absolute optimal performance. We do want the benefit from more accurate statistics and better query plans, but not at the risk of an unusable system. As a result, I've developed the following methodology around managing database statistics for the P6 database. 

1. No Automatic Re-Gathering - The daily, weekly, or other interval of statistic gathering is unlikely to be beneficial. Quite the opposite. It is more likely to cause problems.

2. Smart Re-Gathering - The time to collect statistics is when things have changed significantly. For a new installation of P6, this is happening more often because the data is growing from a few rows to thousands and more. But for a mature system, the data is not changing significantly from week-to-week. There are times to collect statistics:

  • New releases of the application
  • Changes in the underlying hardware or software versions (ex. new Oracle RDBMS version)
  • When additional user groups are added. The new groups may use the software in significantly different ways.
  • After significant changes in the data. This may be monthly, quarterly or yearly. 

3. Always Test - If you take away one thing from this post, it would be to always have a plan to test after changing statistics. In reality, statistics can be collected as often as you desire provided there are tests in place to verify that performance is the same or better. These might be automated tests or simply a manual script of application functions.

4. Have a Way Out - Never change the statistics without a way to return to the previous set. Think of the statistics as one part of the overall application code that also includes the source code--both application and RDBMS. It would be foolish to change to the new code without a way to get back to the previous version.

In the final post, I will talk about the actual script I created for P6 PMDB and possible future direction for managing query performance. 

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