Tuning A Carburetor

Posted in Carburetor Articles by e-Carburetors on October 26, 2011

tuning a carburetor

Tuning Your Oracle Database Using Performance Intelligence Technology

Performance intelligence is technology that uses historical trend data to resolve current database performance problems. It uses millions of distinct data points to identify which issues have most impact on the IT end user. After determining where the issues lie, Oracle tuning can be geared toward a solution.

Learn more about Oracle tuning.

Capturing database performance problems using performance intelligence, Oracle monitor model and a 4 step process. The steps are as follows:

1. Analyzing Trends
Performance intelligence monitoring starts by looking at an analysis of historical data. This analysis can give clues to repetitive issue that add up to interfere with database performance. Only by gathering the past data will you be able to see the whole picture regarding performance.

2. Correlation
The second step of this performance intelligence process is correlating the many types of data being gathered. Performance measurements are then compared across many timeframes to show how specific queries, blocked sessions, or server memory could be accountable for slow downs within the database.

3. Problem Analysis
Correlating data and trends can create a historical analysis of problems within the database. After reviewing these problems, you can then make the appropriate recommendations for how to further tune the database. For IT end users to see the best results possible issues should be prioritized.

4. Alarms
There are alarms in place that will tell a user when an issue has been discovered. They identify the location and severity of the issue so that DBAs can get right to the underlying problem without having to wade through a sea of seemingly unrelated data. Alarms create a clear path for DBAs to follow.

Find out more about the Oracle Monitor model.

Performance intelligence monitoring exposes the conditions truly responsible for bottlenecks. It captures granular data across a period of time to create a depth of analysis that provides DBAs with a full picture of their monitored environments.