Anomaly detection is a major consideration in routine O&M of databases. 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 describes the benefits of the anomaly detection feature and how to view the detection results.
Prerequisites
The database instance that you want to manage is of one of the types described in the following table.
Database instance
Region
ApsaraDB RDS for MySQL
ApsaraDB MyBase for MySQL
Instances in the Philippines (Manila) region are not supported.
ApsaraDB RDS for PostgreSQL
Instances in the China (Nanjing), China (Fuzhou), Thailand (Bangkok), South Korea (Seoul), and Philippines (Manila) regions are not supported.
ApsaraDB RDS for SQL Server
Instances in the China (Nanjing), China (Fuzhou), China (Guangzhou), China (Ulanqab), Thailand (Bangkok), South Korea (Seoul), and Philippines (Manila) regions are not supported.
PolarDB for MySQL Standard Edition or Cluster Edition
Instances in the Thailand (Bangkok), South Korea (Seoul), and Philippines (Manila) regions are not supported.
ApsaraDB for Redis
Instances in the China (Ulanqab) and Philippines (Manila) regions are not supported.
The database instance is connected to DAS and is in the Normal Access state.
NoteFor more information about how to connect a database instance to DAS, see Connect an Alibaba Cloud database instance to DAS.
Benefits
The anomaly detection feature detects exceptions 24/7 based on machine learning and fine-grained monitoring data. This allows DAS to detect database exceptions faster than traditional threshold-based alerting mechanisms.
Item | Traditional solution | DAS anomaly detection |
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 |
Detection method | Fault-driven | Exception-driven |
Periodic detection | Not supported | Automatic and periodic |
Adaptability | Not supported | Adaptive to services that have different characteristics |
Prediction | Not supported | Supported |
View anomaly detection results
In the autonomy center, you can view events that are detected within a specific time range.
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.
Specify a time range to view the exception detection results within the time range.
Enable event subscription
After you enable the event subscription feature for a database instance, DAS sends you a notification every time a subscribed event is triggered. You can specify a notification method such as SMS based on your business requirements. For more information, see Event subscription.
To receive notifications about anomaly events, set the urgency level of the events to Warning. You can specify an urgency level based on your business requirements.
FAQ
Q: How is the change rate of the related metrics in the Analysis of Abnormal Metrics section of the Anomaly Snapshots tab of the Anomaly Detection of Metrics (Time Series Anomaly Detection) event calculated?
A: Change rate of a metric = Actual metric value/Predicated metric value. DAS uses the data at a granularity of hours of a database instance in a specific 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.
References
You can use the autonomy features of DAS to automatically handle database exceptions.