Grafana Faro and xk6-browser are both new tools within the Grafana Labs open source ecosystem, but the pairing is already showing a lot of potential in terms of frontend monitoring and performance testing. Faro, which was announced last November, includes a highly configurable SDK that instruments web apps to capture observability signals that can then be correlated with backend and infrastructure data.
I’ve always been wary about vendor events. In my experience, many of them are mostly marketing pitches, with little or no content that is applicable to my use cases. Despite that, last year I decided to convince my manager to let me attend ObservabilityCON 2022 to see what I could learn from it. My hope was that I would be able to get practical knowledge that could be applied as soon as I got back to work. (Spoiler alert: I did!)
Flaky tests are a problem that are found in almost every codebase. By definition, a flaky test is a test that both succeeds and fails without any changes to the code. For example, a flaky test may pass when someone runs it locally, but then fails on continuous integration (CI). Another example is that a flaky test may pass on CI, but when someone pushes a commit that hasn’t touched anything related to the flaky test, the test then fails.
At Grafana Labs, we love tracing, which is why we’ve been hard at work on Grafana Tempo, an open source, highly scalable distributed tracing backend. Tempo just had its 2.0 release. In conjunction with that release, we are excited to show off TraceQL — a powerful new query language designed for distributed tracing. In this blog, we’ll provide an overview of why we created TraceQL, how it works, how you can put it to use today, and what we have planned for future iterations.
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.
