We recently launched a new design for our technical documentation. The goal of the redesign was to make our technical documentation more accessible, modern, and scalable as we grow. In addition to a new look (hello, new typeface and layout!), our updated docs pages reveal the underlying work our team has done to evolve and enhance our technical documentation.

While more and more teams are adopting Kubernetes as their standard container orchestration technology, cost insight is lacking. Teams often don’t know how much they’re spending, where in their organization they are spending, or what is driving their infrastructure cost increases. OpenCost helps alleviate this problem by bringing real-time cost monitoring to Kubernetes workloads with a solution that encompasses both an open specification and an open source project.

Grafana Tempo 2.0 is finally here, and it’s being released with two new important features. It took us longer than we would have liked to get this release going, but it turns out that rewriting your backend AND building a new query language is quite difficult. Thanks to a massive team effort, we are proud to release Tempo with support for TraceQL and with Apache Parquet as the default backend storage format. Read on to get a quick overview of this huge release.

Here at Grafana Labs, we aim to create products which integrate well with open standards and are easy to install everywhere. Today, we’re excited to announce Grafana Agent v0.31, which allows you to connect to even more types of observability signals for both scraping and remote writes. And to help you install the Agent more easily, there is now an official Windows Docker image and an official Helm Chart. Here’s a breakdown of the latest features and upgrades in Grafana Agent v0.31.

Application performance monitoring (APM) involves a mix of tools and practices to track specific performance metrics. Engineers use APM to monitor and maintain the health of their applications and ensure a better user experience. This is crucial to high quality architecture, development, and operations, but it can be difficult to achieve in Kubernetes since the container orchestration system doesn’t provide an easy way to monitor application data like it does for other cluster components.

Kubernetes makes it easier for businesses to automate software deployment and manage applications in the cloud at scale. However, if you’ve ever deployed a cloud native app, you know how difficult it can be to keep it healthy and predictable. DevOps teams and SREs often use distributed tracing to get the insights they need to learn about application health and performance.

Kubernetes monitoring can be difficult and complex. In order to determine the health of your project at every level, from the application to the operating system to the infrastructure, you need to monitor metrics in all the different layers and components — services, containers, pods, deployments, nodes, and clusters.

ESnet (Energy Sciences Network) is a high-performance network backbone built to support scientific research. Funded by the U.S. Department of Energy and part of Lawrence Berkeley National Laboratory, ESnet provides fast, reliable connections between national laboratories, supercomputing facilities, and scientific instruments around the globe. Our mission is to allow scientists to collaborate and perform research without worrying about distance or location.

Kubernetes, a graduated project of the Cloud Native Computing Foundation (CNCF) ecosystem, is the most prominent and widely used container orchestration systems. It’s used to manage and deploy containers in a wide range of environments, from IoT devices based on Raspberry Pis to enterprise environments consisting of millions of services.

Kubernetes has become the preferred tool for DevOps engineers to deploy and manage containerized applications on one or multiple servers. These compute nodes are also known as clusters, and their performance is crucial to the success of an application. If a Kubernetes cluster isn’t performing optimally, the application’s availability and performance will suffer, leading to unhappy users and even revenue loss.