Kristian Zhelyazkov is a developer at SAP working on Gardener, the SAP-driven Kubernetes-as-a-service open source project. In this guest blog post, he explains why the project is moving its logging stack to Loki.
Amazon’s Elastic Compute Cloud (AWS EC2) is one of the most popular ways to run applications in the cloud, but finding logs for a given instance is a common struggle. That’s where Loki can help. With Loki aggregation, you can group all your logs from all your virtual machines in one place, and with its search capabilities, you can quickly find and analyze them. It’s a great way to gain visibility in your cloud deployment.
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?
