Granular resource metrics (memory, CPU, load, etc.) are important for identifying issues with Kubernetes microservices, but these metrics can be convoluted and difficult to use. The best KPIs to help you easily identify service issues are API metrics, such as request rate, call error, and latency. These metrics will quickly locate degradations in a component within a microservices application.

Having a single pane of glass for monitoring your Kubernetes metrics is a best practice because it allows you to view all of these metrics in a single, unified interface. This can make it easier to monitor and manage your cluster, as you can see all of the relevant metrics and data in one place, rather than having to switch between multiple tools and interfaces.

Having a single pane of glass for monitoring your Kubernetes metrics can also help you to identify trends and patterns in your data more easily. With all of your metrics in one place, you can see how different metrics are related and how they change over time, which can help you to identify potential issues and take action to address them.