Use an intelligent detector to detect data anomalies

Detect abnormal data fluctuations of a Prometheus instance

1. Log on to the ARMS console.
2. In the left-side navigation pane, choose Prometheus Monitoring > Prometheus Instances.
3. In the upper-left corner of the page, select the region where the Prometheus instance resides and click the dashboard of the Prometheus instance.
4. In the left-side navigation pane, click the Explore icon. In the upper-left corner of the page, select a data source from the Explore drop-down list.
5. Select a metric from the Metrics drop-down list to view the time-series data of the metric. For example, you can select the following metric whose data anomalies you want to view:

   arms_cms_collector_duration_seconds

   
6. In the text box on the right of the Metrics drop-down list, enter the following PromQL statement. The statement is used as a sample anomaly detector. This way, the abnormal data fluctuations about the metric over a specific period of time can be identified.

   anomal_detect (arms_cms_collector_duration_seconds[180m],3)

   Note

   • arms_cms_collector_duration_seconds: the name of the metric used in the previous step. You can replace it based on your business scenario.
   • The data type of the metric that the PromQL statement queries must be range vector. Therefore, you must append a time selector to the right of the metric name. The default value of the time selector is [180m]. The default value of the parameter is 3. If you execute other aggregate functions in advance, you must modify the time selector to [180m:] in the PromQL statement. This way, you can change the data type of the metric to range vector. For example, you can modify the PromQL statement to anonym_detect (sum(node_memory_free_bytes)[180m:],3).