The blog post explores seven essential best practices for Kubernetes monitoring, guiding DevOps and Site Reliability Engineers (SREs) in developing robust monitoring strategies. It differentiates between monitoring and observability, emphasizing the importance of defining clear objectives, identifying critical metrics, selecting appropriate tools, and implementing comprehensive monitoring across system and application levels. The guide covers key aspects such as choosing between open-source and commercial solutions, monitoring the monitoring system itself, managing data storage, tracking the Kubernetes control plane, and integrating monitoring with incident response.

This definitive guide provides a comprehensive walkthrough for installing Prometheus on Kubernetes, covering essential steps from prerequisites to advanced configuration. Readers will learn how to leverage Helm charts, create custom scrape configurations, manage resources, and implement best practices for Kubernetes monitoring. The tutorial offers practical code examples, troubleshooting tips, and insights into transforming cluster observability through powerful, open-source monitoring techniques.

Helm dry run, using the helm install --dry-run command, is a valuable technique for validating Helm charts before deployment on a Kubernetes cluster. It helps avoid errors and unexpected behavior by simulating the installation process without modifying the cluster. Helm dry run works alongside other Helm commands like helm template and helm lint to streamline development and ensure charts are well-structured, compatible, and ready for deployment.

As the complexity of a Kubernetes cluster grows, managing resources such as CPU and memory becomes more challenging. Efficient pod scheduling is critical to ensure optimal resource utilization and enable a stable and responsive environment for applications to run in. In this blog, we will delve into the intricacies of pod scheduling, including optimization of resource allocation and balancing workloads.

Partitioning is a way to divide GPU resources into smaller slices. This allows Pods to be scheduled only on the memory/compute resources they actually need, thus increasing GPU utilization and reducing infrastructure costs in Kubernetes clusters.

FAUN is glad to announce a partnership with ContainerDay Security.