In this article, we'll explore another monitoring use case. This particular use case is exciting– it's MetricFire's use case with Kubernetes and Linode! We'll examine how our company uses our platform to monitor our K8s clusters hosted on Linode. Let's get started!

Prometheus and its querying language, PromQL, have several functions that perform various calculations on their data. One of the most widely used functions is rate(); however, it is also one of the most misunderstood.

Monitoring is the most important aspect of infrastructure operations. Effective monitoring strategies help optimize infrastructure usage, improve planning, and resolve incidents quickly. While monitoring preceded DevOps, DevOps has further transformed the software development process to the extent that monitoring also has to evolve.

In this article, we will be looking into Kubernetes monitoring with Graphite and Grafana. Specifically, we will look at how your whole Kubernetes set-up can be centrally monitored through Hosted Graphite and Hosted Grafana dashboards. This will allow Kubernetes Administrators to centrally manage all of their Kubernetes clusters without setting up any additional infrastructure for monitoring.

Kubernetes (K8s) is a popular container orchestration solution, but monitoring its performance can be quite challenging. Luckily, there's a solution that makes it easier - MetricFire. It's a cloud-based monitoring and visualization platform that provides comprehensive metrics, alerts, and dashboards for K8s clusters. The platform offers amazing cloud-based monitoring and visualization services that can make the K8s monitoring seamless.

Monitoring is the most important aspect of infrastructure operations. Effective monitoring strategies help optimize infrastructure usage, improve planning, and resolve incidents easily. While monitoring preceded DevOps, DevOps has further transformed the software development process to the extent that monitoring has to evolve as well.

The growing adoption of microservices architecture also drives the adoption of containers to package, distribute and run the microservices. This requires orchestrators to handle the availability, performance, and deployments of those containers on the server. However, the entire setup around microservices, containerization, and orchestrators complicates logging and monitoring since various distributed and diversified applications interact with each other.

Kubernetes is now the industry standard for cloud-based organizations. Slowly, many enterprises and mid-level companies are adopting it as the default platform for managing their applications. But we all know Kubernetes adoption has its challenges, as well as its associated costs. How do we decide when and what to migrate to Kubernetes? Does migrating to Kubernetes mean overhauling all DevOps processes? Adopting K8S should not lead to an overhaul of your DevOps process - it should complement it.

In this article, we will examine a Kubernetes metrics server and its uses. We will also learn how to set one up and use it to monitor Kubernetes metrics. Finally, we will explore using Hosted Graphite by MetricFire to monitor Kubernetes metrics. To easily get started with monitoring Kubernetes clusters, check out our tutorial on using the Telegraf agent as a Daemonset to forward node/pod metrics to a data source and use that data to create custom dashboards and alerts.

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Implementing a monitoring solution is no small task; with your company's data growing, it can feel impossible. Often, small teams develop their own monitoring infrastructure because it is more cost-effective than a platform. But when your business grows, your data does, too. Monitoring can quickly become a bigger challenge than a small team can handle, and some companies need more resources to hire a developer dedicated to their monitoring.

Kubernetes is used in production-level applications and software services to automate the deployment, scaling, and management of containerized applications - ensuring high availability and consistent performance across distributed systems. It enhances reliability through features like load balancing, self-healing, and rolling updates, enabling efficient resource utilization and orchestration in cloud-native and hybrid environments.