Database Autonomy Service (DAS) provides the anomaly detection feature to detect exceptions 24/7 based on machine learning and fine-grained monitoring data. This detection mechanism allows DAS to detect database exceptions faster than traditional threshold-based alerting mechanisms. This topic provides the benefits of this feature and describes how to view the detection results.
- The database instance that you want to manage is of one of the following types:
- ApsaraDB RDS for MySQL
- ApsaraDB MyBase for MySQL
- ApsaraDB RDS for PostgreSQL
- ApsaraDB for Redis instance
- PolarDB for MySQL Cluster Edition
- The database instance is connected to DAS and is in the Accessed state. Note For more information about how to connect a database instance to DAS, see Access instances.
|Method||Rule- or threshold-based||AI-based|
|Monitored objects||Metrics||A wide range of objects, such as metrics, SQL statements, logs, locks, and O&M events|
|Latency||From 5 minutes to one or more days||Quasi-real-time|
|Periodic detection||Not supported||Automatic and periodic|
|Adaptability||Not supported||Adaptive to services that have different characteristics|
View anomaly detection results
In the autonomy center, you can view events that are detected within a specific time range. For example, you can view exceptions, optimization suggestions, auto scaling events, and other events.
- Log on to the DAS console.
- In the left-side navigation pane, click Instance Monitoring.
- On the page that appears, find the database instance that you want to manage and click the instance ID. The instance details page appears.
- In the left-side navigation pane, click Autonomy Center.
- Select a time range to view the anomaly detection results within the specified time range.
Change rate of the metric = Actual metric value/Predicated metric value. DAS uses the data at a granularity of hours of a database instance in a specific past period of time to predict the metric value of the database instance in the current time range. The predicted metric value is used as a baseline and compared with the actual metric value. This way, the change rate of the metric is calculated.