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
Region
ApsaraDB RDS for MySQL
ApsaraDB MyBase for MySQL
China (Hangzhou), China (Shanghai), China (Shenzhen), China (Heyuan), China (Guangzhou), China (Qingdao), China (Beijing), China (Zhangjiakou), China (Hohhot), China (Ulanqab), China (Nanjing - Local Region), China (Fuzhou - Local Region), China (Chengdu), China (Zhengzhou - Local Region), China (Hong Kong), Japan (Tokyo), South Korea (Seoul), Singapore, Malaysia (Kuala Lumpur), Indonesia (Jakarta), Philippines (Manila), Thailand (Bangkok), UAE (Dubai), SAU (Riyadh - Partner Region), Germany (Frankfurt), US (Silicon Valley), US (Virginia), and UK (London)
ApsaraDB RDS for PostgreSQL
China (Hangzhou), China (Shanghai), China (Shenzhen), China (Heyuan), China (Guangzhou), China (Qingdao), China (Beijing), China (Zhangjiakou), China (Hohhot), China (Ulanqab), China (Chengdu), China (Hong Kong), Japan (Tokyo), Singapore, Malaysia (Kuala Lumpur), Indonesia (Jakarta), Philippines (Manila), UAE (Dubai), Germany (Frankfurt), US (Silicon Valley), US (Virginia), and UK (London)
ApsaraDB RDS for SQL Server
China (Hangzhou), China (Shanghai), China (Shenzhen), China (Heyuan), China (Qingdao), China (Beijing), China (Zhangjiakou), China (Hohhot), China (Ulanqab), China (Chengdu), China (Hong Kong), Japan (Tokyo), Singapore, Malaysia (Kuala Lumpur), Indonesia (Jakarta), UAE (Dubai), Germany (Frankfurt), US (Silicon Valley), US (Virginia), and UK (London)
PolarDB for MySQL Standard Edition and Cluster Edition
China (Hangzhou), China (Shanghai), China (Shenzhen), China (Heyuan), China (Guangzhou), China (Qingdao), China (Beijing), China (Zhangjiakou), China (Hohhot), China (Ulanqab), China (Chengdu), China (Hong Kong), Japan (Tokyo), Singapore, Malaysia (Kuala Lumpur), Indonesia (Jakarta), Philippines (Manila), Germany (Frankfurt), US (Silicon Valley), US (Virginia), and UK (London)
Tair (Redis OSS-compatible)
Redis Open-Source Edition instances
Tair (Enterprise Edition) DRAM-based instances
China (Hangzhou), China (Shanghai), China (Shenzhen), China (Heyuan), China (Guangzhou), China (Qingdao), China (Beijing), China (Zhangjiakou), China (Hohhot), China (Chengdu), China (Hong Kong), Japan (Tokyo), South Korea (Seoul), Singapore, Malaysia (Kuala Lumpur), Indonesia (Jakarta), Philippines (Manila), Thailand (Bangkok), UAE (Dubai), SAU (Riyadh - Partner Region), Germany (Frankfurt), US (Silicon Valley), US (Virginia), and UK (London)
Tair (Enterprise Edition) persistent memory-optimized and ESSD/SSD-based instances
China (Hangzhou), China (Shanghai), China (Shenzhen), China (Beijing), China (Zhangjiakou), China (Hong Kong), Singapore, Germany (Frankfurt), and US (Virginia)
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. Compared with rule-based or threshold-based alerting, you can detect database exceptions in a more timely manner.
Item | Traditional solution | DAS anomaly detection |
Method | Rule-based 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.
On the instance details page, click Autonomy Center in the left-side navigation pane.
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 text messages 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 allow DAS to automatically handle database exceptions. For more information, see the following topics: