Google Kubernetes Engine | Google Cloud latest news and announcements

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AN IMPORTANT ASPECT OF SUPPORTING ANY APPLICATION IS THE POSSIBILITY OF OBSERVING THE STATE OF THE APPLICATION AND ITS PERFORMANCE, AND THE BASE INFRASTRUCTURE IN ORDER TO RESOLVE PROBLEMS AS THEY APPEAR.

Google Kubernetes Engine (GKE)

Google Kubernetes Engine (GKE) is one of the most advanced container application management solutions. It provides the most features and automated options compared to competing services.

The main advantages of the GKE

- intuitive dashboard integrated with monitoring (Stackdriver),
- automatic updates of the control panel and nodes,
- automatic node repair,
- the most extensive solution for automatic scaling,
- identity and access management features that allow you to better protect sensitive workloads,
- Autopilot service to simplify cluster creation and management.

 

But how will GKE help in monitoring services ...

Google Kubernetes Engine (GKE) already provides audit logs, operational logs, and metrics along with dashboards and automatic error reporting to help you run reliable applications. By using these logs and metrics, Cloud Operations provide alerts, monitoring dashboards, and a log explorer to quickly detect and resolve problems.

 

Introduction to Kubernetes control plane metrics

In addition to these existing telemetry data sources, Google has released Kubernetes control plane metrics that are now generally available. With GKE, Google fully manages the Kubernetes control plane, however, when troubleshooting problems, it may be helpful to have access to certain metrics emitted by the Kubernetes control plane.

Google in its vision wants to make Kubernetes easier to use and easier to maintain, these control plane metrics are directly integrated with Cloud Monitoring, so you don't need to manage any metrics collection or scrape setup.

For example, to understand if the API server is working properly, you can use metrics like apiserver_request_total and apiserver_request_duration_seconds to track API server load, the fraction of API server requests that return errors, and response latency for requests received by the API server. Also, apiserver_storage_objects can be very useful for monitoring API server load, especially if you are using custom controllers. Breaking down this metric by resource label shows which custom Kubernetes resource or controller is problematic. This is just one of the uses.

 

Kubernetes control plane metrics can also be configured using Terraform

 

 

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