Nothing is more frustrating than feeling like you’ve finally found the perfect trace only to see that you’re missing critical spans. In fact, a common question for new users and operators of Jaeger, the popular distributed tracing system, is: “Where did all my spans go?” In this post we’ll discuss how to diagnose and correct lost spans in each element of the Jaeger span ingestion pipeline.
Over the past few years, NGINX users have naturally gravitated toward Grafana, and vice versa. These days, it’s not uncommon to see these two open source tools used together in the wild. And for good reason. F5, which acquired NGINX last year, is prioritizing building visibility across the entire product set, to make it easy for customers to quickly gain the insights that they need. Meanwhile, Grafana has evolved into the primary visualization and analysis tool in the open source market.
Dashboard sprawl is a real problem whether you’re using Grafana or any other tool. When growing to thousands of users – and as many dashboards – you’ll eventually want more information about how the tool is being used in your organization. After all, dashboards don’t help anyone if they aren’t being used. Managing large installations is one of the areas where Grafana Enterprise improves Grafana, and our launch of usage insights in 7.0 is a key part of that.
Hi folks! Éamon here. I’m a recent-ish addition to the Solutions Engineering team and just getting my feet wet on the blogging side so bear with me. :) Back in March, we introduced the Grafana Cloud Agent, a remote_write-focused Prometheus agent. The Grafana Cloud Agent is a subset of Prometheus without any querying or local storage, using the same service discovery, relabeling, WAL, and remote_write code found in Prometheus.
The classic debate when running a software as a service (SaaS) business is between release frequency vs. stability and availability. In other words, are you Team MTTR (mean time to recovery) or Team MTBF (mean time between failure)? In this blog post, I argue for MTTR, which encourages you to push more frequently, embrace the instability this may introduce, and invest in training and tooling to deal with the pursuing outages.
We all know that Prometheus is a popular system for collecting and querying metrics, especially in the cloud native world of Kubernetes and ephemeral instances. But people forget that Java has been running enterprise software since 1995, while Prometheus is a relative newcomer to the scene. It was only created in 2012! Even though Java has had its own metric collectors since before Prometheus was born, none of our new environments speak its (metric) language. How can you bridge that gap?
There are a lot of great things about Grafana 7.0, but one of my favorite features is the new React-based plugin platform, which has a set of new APIs and design system to help you build your own plugin. The process is easier and faster than ever. In this blog post, I’ll show how you can create a panel plugin for visualizing scatter plots. A scatter plot is a type of graph that displays values for (usually) two variables as a set of points along a horizontal and vertical axis.
Do you have a Prometheus histogram and have you asked yourself how to visualize that histogram in Grafana? You’re not alone. Here, we will show you how it’s done. This post assumes you already have a basic understanding of Prometheus and Grafana and it will look at Prometheus histograms from the perspective of Grafana 7.0.
As many of you already know, we created Grafana using AngularJS, but we have been migrating to React for about two years now. One of the big missing pieces in our migration puzzle was the templating system. This post starts in late 2019 when I first got my hands on this mysterious and complex area of the Grafana code base.
Tracing is a widely adopted solution to provide performance insights into distributed applications. It is a valuable resource for developers to view the service call graph and track service latency at a granular level. It’s also a handy tool for on-call engineers to drill down and debug a problematic service during an outage. There are a number of open source distributed tracing frameworks out in the wild, including Jaeger, Zipkin, and OpenTelemetry.
