Expert Oracle Posts

The Oracle’s Automatic Workload Repository (AWR) collects, processes, and maintains performance statistics for problem detection and self-tuning purposes.  The report generated by AWR is a big report and it can take years of experience to actually understand all aspects of this report. In this post we will try to explain some important sections of AWR, significance of those sections and also some important tips. Please note that explaining all sections of AWR will not be possible so we will stick to some of the most frequently used sections.

Note that this is not comprehensive information and goal is to help in giving an overview of few key sections to Junior DBAs as a primer and to encourage them to build further the knowledge in related fields.

The active session history (ASH) sampler performs the sampling. ASH samples the current state of all active sessions. The database collects this data into memory, where you can access it with a V$ view.

Using ASH enables you to examine and perform detailed analysis on both current data in the V$ACTIVE_SESSION_HISTORY view and historical data in the DBA_HIST_ACTIVE_SESS_HISTORY view, often avoiding the need to replay the workload to gather additional performance tracing information.

ASH also contains execution plan information for each captured SQL statement. You can use this information to identify which part of SQL execution contributed most to the SQL elapsed time. The data present in ASH can be rolled up on various dimensions that it captures, including the following:

The Automatic Workload Repository (AWR) automatically persists the cumulative and delta values for most of the statistics at all levels except the session level.

This process is repeated on a regular time period and the result is called an AWR snapshot. The delta values captured by the snapshot represent the changes for each statistic over the time period.

The Automatic Workload Repository (AWR) collects, processes, and maintains performance statistics for problem detection and self-tuning purposes. This data is both in memory and stored in the database.