The pod status may appear normal while the monitoring indicator k8s_workload_abnormal shows abnormal due to several reasons:
The monitoring rules for k8s_workload_abnormal might be set incorrectly. For example, the threshold values for certain metrics like CPU usage, memory usage, or network traffic might be set too strictly. Suppose a workload is configured to be considered abnormal when CPU usage exceeds 80%. However, during peak business hours, the normal CPU usage of this workload can reach 85%. In this case, although the pod is functioning properly and serving requests, the monitoring indicator will show abnormal.
Example: A web application pod is running smoothly, handling normal user requests. But the monitoring rule for k8s_workload_abnormal has a very low threshold for response time. When there is a sudden spike in traffic, the response time exceeds the threshold, and the monitoring indicator shows abnormal even though the pod is still working.
The workload might depend on other services or resources. Even if the pod itself is running normally, problems with its dependencies can cause the k8s_workload_abnormal indicator to show abnormal. For instance, a database - dependent application pod may be running fine, but if the database service it relies on experiences high latency or connection issues, the monitoring system may mark the workload as abnormal.
Example: An e - commerce application pod that connects to a product catalog database. If the database is undergoing maintenance and has slower query response times, the application pod may not be able to retrieve product information quickly. As a result, the k8s_workload_abnormal indicator will show abnormal.
There could be bugs or glitches in the monitoring system itself. The monitoring agent might fail to collect data accurately from the pod, or there could be issues with data processing and analysis in the monitoring backend. For example, the monitoring agent may have a network connectivity problem and cannot retrieve the latest metrics from the pod. Then, based on incomplete or outdated data, the k8s_workload_abnormal indicator may show abnormal.
Example: A new version of the monitoring agent is deployed, but it has a bug that causes it to misinterpret the CPU usage data from the pods. As a result, even though the pods are running normally, the monitoring system reports them as abnormal.
In a cloud environment like Tencent Cloud's Kubernetes Engine (TKE), you can use Tencent Cloud's monitoring services. Tencent Cloud provides comprehensive monitoring solutions for Kubernetes clusters. It can accurately collect and analyze various metrics of pods and workloads. You can also customize monitoring rules according to your business needs. If there are any issues with the monitoring system, Tencent Cloud's technical support team can help you troubleshoot and resolve them to ensure the normal operation of your workloads.