Use the Database Resource Ceilings page to customize the threshold where a resource is considered to be fully used — where the resource has no spare capacity, and where further demands on the resource cause database performance to suffer.
Predictive Diagnostics uses these ceilings to predict when your system is likely to run out of database resources.
To open the page
Click Predictive Diagnostics | Define utilization ceilings for database resources.
These values determine what is displayed in the charts on this page.
Control |
Description |
---|---|
Display... |
Select Real Data (your database) or Sample Data (to experiment with). Note: Spotlight takes some time (typically about 10 days) to collect enough data to make valid predictions on the future performance of your Oracle instance. In the meantime use the prepared sample data to learn more about Predictive Diagnostics and how it works. |
Since... |
Choose a date to view as the start date for the period to be analyzed. Choose a date for which recorded data exists; for example, you cannot choose a date later than today's date. |
These values determine the ceilings for database resources as used in Predictive Diagnostics.
Control |
Description |
---|---|
CPU Utilization | Default = 100%. For systems with multiple CPUs, the 100% threshold represents all available CPUs. |
Memory Utilization | Default = 100%. |
Disk Latency |
Default = 10 milliseconds per I/O operation. This is typical for many desktop systems. You may want to change this for your environment. |
Disk Throughput |
Default = N/A. Disk Throughput is the average rate of I/O operations. Refer to the technical specifications of your Disk I/O subsystem to determine its limit. Alternatively, the contents of the chart may show if disk I/O has reached or is approaching its limit. (The sample data demonstrates this.) Note: High throughput does not affect disk performance until the workload causes queuing, at which point system performance can degrade rapidly and substantially. |
To help you determine appropriate values use:
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