Operations | Monitoring | ITSM | DevOps | Cloud

By running performance tests continuously and automatically, you can identify and correct performance regressions as they occur. One way to do this is by integrating performance testing into your development process. In this step-by-step post, we explore how to do just that, using Grafana k6 and GitHub Actions. k6 is an open source load testing tool to test the performance of APIs, microservices, and websites.

It’s summertime in the Northern Hemisphere, and for many people that means it’s also travel season. But before you depart for your dream holiday, don’t forget the essentials: passport, suitcase, and … Grafana? That’s right. If you’re headed to a different country, odds are you’ll use a different currency when you get there. And you can use Grafana to track changes in the exchange rates so you can get the most bang for your buck.

At Grafana Labs we believe in shipping features early and often, and in recent years we’ve doubled down on that philosophy. We no longer wait for the yearly major release to give you access to the next big thing. Instead, we regularly make new features, bug fixes, and security patches available to our self-managing users (Grafana OSS and Grafana Enterprise) throughout the year.

Continuous integration and continuous deployment (CI/CD) are the backbone of modern software delivery, but there’s still limited visibility into their processes. Here’s how that’s changing with OpenTelemetry (OTel), and why those changes are so exciting.

Michal Vaško is a DevOps engineer at cloudWerkstatt, with a passion for open source technology and a deep love for observability. While operations or platform teams have long relied on visibility into metrics to react swiftly, the idea of doing the same thing with logs was once just a dream. Thankfully, Grafana Loki has revolutionized the logging stack, giving you the same level of visibility with logs that you get with metrics.

At its core, observability is about helping humans understand and optimize complex systems. It enables engineering teams to ask questions on the fly, and to learn not only when something goes wrong but why. Observability also allows organizations to proactively identify and address performance issues — before their end users even have a chance to notice.

At Grafana Labs, our No. 1 approach when building AI/ML tools is to enable humans (a.k.a. all of us!) to understand complex systems. In other words, we want to make observability still human, but less complicated. (Our second use case? Making social media more fun.) We believe that AI/ML tools in observability should work towards minimizing toil and the need for everyone in your organization to have the same deep domain knowledge about your increasingly complex stack.