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Oracle Predictive Modeling

Oracle Tips by Burleson Consulting

"Predictions are difficult, especially about the future". Yogi Berra

One feature that makes Oracle the most powerful database ever created is his ability to create predictive models to assist the DBA.  These powerful predictive models allow Oracle to forecast the benefit of changes to the database:

  • SGA Advisors - Oracle has predictive advisories for the data buffer cache, the shared pool and almost every region within the Oracle RAM heap.  In Oracle 10g automated memory management (AMM), Oracle acts upon the predictions and morphs the SGA automatically.

  • Tuning advisors - Oracle's automatic diagnostic database advisor (ADDM) uses artificial intelligence to recommend new materialized views and indexes.  Oracle also has a SQLTuning advisor and a SQLAccess advisor.  These advisors gather real-world empirical execution information from Oracle and recommends actions based upon the execution samples.

Is it possible for Oracle to predict a future problem and prevent it?  The adage "Those who forget the past are condemned to repeat it" is relevant here, and it's easy to develop a predictive model that will analyze historical patterns and "proactively" change your database BEFORE the problem occurs.

This approach is used in the Ion analyzer, where historical data from STATSPACK or AWR is analyzed to identify the signatures (repeating patterns).  You can then interface with dbms_scheduler to change the database just-in-time to prevent the future outage.  This revolutionary techniques is the basis of his new book "Oracle Tuning: The Definitive Reference".  

Oracle is listening, and it appears that Oracle will soon leverage upon their wonderful time-series performance repository, the AWR.

In an OracleWorld 2003 presentation titled Oracle Database 10g: The Self-Managing Database by Sushil Kumar of Oracle Corporation, Kumar states that the Automatic Maintenance Tasks (AMT) Oracle10g feature will automatically detect and re-build sub-optimal indexes.

In a superb paper titled METRIC BASELINES: DETECTING AND EXPLAINING PERFORMANCE EVENTS IN EM 10GR2, Presented at the RMOUG 2005 Training Days in Denver, John Beresniewicz of Oracle Corporation gives us a great preview into the new predictive modeling tools in Oracle 10g release 2 (10.2).  Beresniewicz told me that the use of "baselines to capture and adapt thresholds to expected time-dependent workload variations" is a core feature of the next release of Oracle: 

The metric baselines introduced in Enterprise Manager 10gR2 statistically characterize specific system metrics over time periods matched to system usage patterns. These statistical profiles of expected metric behavior are used to implement adaptive alert thresholds that can signal administrators when statistically unusual metric events occur.

Assuming that systems are normally stable and performance problems rare, it is reasonable to expect that actual performance events will be highly correlated with observed unusual values in some metric or other. Thus it is hoped that baseline-driven adaptive thresholds will both reduce configuration overhead for administrators and more reliably signal real problems than fixed alert thresholds.

The idea is simple.  Because Automated Workload Repository (AWR, or STATSPACK in 8i and ) keeps a historical performance record, Oracle now knows when a scheduled task is going to cause a resource shortage, before it happens!  Using the AWR we can analyze statistically valid trends and repeating patterns.  Many databases have batch job schedules, implemented via crontabs, dbms_job or dbms_scheduler, and this valuable data can be used to:

  • Automatically morph the Oracle instance to anticipate a daily of weekly batch job.  The most common example of this is a database that runs in OLTP mode during the day (first_rows) and switches to decision support at night (all_rows).

  • Predict the reduction in logical I/O and physical I/O from reorganizing a table or index

  •  Use linear regression to predict a future time when Oracle will exhaust server resources RAM, CPU)

Beresniewicz also shows the value of signature analysis where you average metric values by day-of-the-week and hour-of-the-day, and use the valid trends to create a predictive model for proactive DBA changes:

The baseline period can be divided into sub-intervals of time over which statistical aggregates are computed. These subintervals, or time partitions are intended to allow baselines to capture and adapt thresholds to expected time-dependent workload variations.

EM 10gR2 allows for simple time partitioning that can capture common daily or weekly usage/workload cycles. The daily options are:

By hour of day: aggregate each hour separately, strong variation across hours

By day and night: aggregate the hours of 7am-7pm as day and 7pm-7am as night

By all hours: aggregate all hours together, no strong daily cycle

The weekly time partitioning options are: 

By day of week: aggregate days separately, strong variation across days

By weekday and weekend: aggregate Mon-Fri together and Sat-Sun together

By all days: aggregate all days together, no strong weekly cycle

 Time partitioning is fully specified in EM 10gR2 by selecting both daily and weekly partitioning options.

For example, consider an operational system serving many online users working similar daily schedules and running batch jobs in the evening.

In this exciting paper we see that Oracle is planning to adopt the same kind of predictive model that I have espoused for many years.  The basic idea is that historical data will provide valid predictive information that can be used by Oracle to fix a resource issue BEFORE it cripples your performance. 

Practical Application of predictive modeling

Predictive modeling is widely used in the credit card industry:

- Predictive models exist that will accurately determine your age, gender and race only from examining your credit card purchase history.

- Using predictive modeling we can even "guess" your income range, number of dependant and even your weight.

- Credit card companies can calculate the odds you eat at McDonald's today, considering you ate at McDonald's once every X day.

- The credit card companies use "cohorts", statistical groupings of related purchasers. They give these cohorts cute names like "dinks (double income, no kids):

- Credit card fraud prevention software is so sophisticated that if you order products a person in your group never ordered, your card will get automatically locked.

- Credit card use is never private and it can be used by the police as credit card purchase history is used to prove DUI (you took a large tab at a bar) indirectly.

If you like Oracle tuning, see the book "Oracle Tuning: The Definitive Reference", with 950 pages of tuning tips and scripts. 

You can buy it direct from the publisher for 30%-off and get instant access to the code depot of Oracle tuning scripts.



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