Kubernetes Intelligent Inspection¶
Based on intelligent inspection algorithms, through regular monitoring of key metrics such as total Pod count, Pod restart count, and API Server QPS, Kubernetes Intelligent Inspection can promptly detect and predict potential issues within a cluster. This method not only identifies abnormal fluctuations in resource usage but also precisely pinpoints the source of problems through root cause analysis, whether it's configuration errors, resource mismatches, or excessive requests. It makes the operation and maintenance of Kubernetes clusters more intelligent and automated.
Use Cases¶
Gain deep insights into various performance metrics of the cluster, providing comprehensive monitoring capabilities from cluster resources and service resources to the API server level.
Configuration¶
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Define the monitor name.
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Select the inspection scope: Filter based on cluster, namespace, and host to limit the data range for inspection. One or more tags can be added. If no filter is added, all metric data will be inspected.
Viewing Events¶
The monitor retrieves metric information from the monitored application service objects for the last 10 minutes. When abnormal situations are identified, corresponding events are generated. These abnormal events can be viewed in the Events > Intelligent Monitoring list.
Event Details Page¶
Click on an Event to view the details page of an intelligent monitoring event, including the event status, time of anomaly occurrence, anomaly name, analysis report, alert notifications, history, and related events.
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Click Jump to Monitor in the upper right corner to view and adjust the Intelligent Monitor Configuration.
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Click the Export button in the upper right corner to choose between "Export JSON File" and "Export PDF File", allowing you to obtain all key data corresponding to the current event.
Analysis Report
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Anomaly Summary: Displays statistics on the distribution of abnormal API Server nodes in the current cluster.
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Anomaly Analysis: Allows viewing information such as the number of API Server nodes, API QPS, number of read requests being processed, write request success rate, and number of write requests being processed.