For many years, it has been possible to scale Cortex clusters to hundreds of replicas. The relatively simple Dynamo-style replication relies on quorum consistency for reads and writes. But as such, more than a single replica failure can lead to an outage for all tenants. Shuffle sharding solves that issue by automatically picking a random “replica set” for each tenant, allowing you to isolate tenants and reduce the chance of an outage.

Whether it’s during an incident to find the root cause of the problem or during development to troubleshoot what your code is doing, at some point you’ll have an issue that requires you to search for the proverbial needle in your haystack of logs. Loki’s main use case is to search logs within your system. The best way to do this is to use LogQL’s line filters. However, most operators are case sensitive.

Daniel is a Site Reliability Engineer at k6.io. He’s especially interested in observability, distributed systems, and open source. During his free time, he helps maintain Grafana Tempo, an easy-to-use, high-scale distributed tracing backend. Distributed tracing is a way to track the path of requests through the application. It’s especially useful when you’re working on a microservice architecture.

We built Grafana Enterprise Metrics (GEM) to empower centralized observability teams to provide a multi-tenanted, horizontally scalable Prometheus-as-a-Service experience for their end users. The GEM plugin for Grafana is a key piece of realizing this vision. It provides a point-and-click way for teams operating GEM to understand the state of their cluster and manage settings for each of the tenants within it.

I have been using Grafana for almost four years now, and in that time it has become my go-to tool for my application observability needs. Especially now that Grafana allows you to also view logs and traces, you can easily have all three pillars of observability surfaced through Grafana. As a result, when I started working on the Elixir PromEx library, having Grafana be the end target for the metrics dashboards made perfect sense.

Since we launched Grafana Enterprise Metrics (GEM), our self-hosted Prometheus service, last year, we’ve seen customers run it at great scale. We have clusters with more than 100 million metrics, and GEM’s new scalable compactor can handle an estimated 650 million active series. Still, we wanted to run performance tests that would more definitively show GEM’s horizontal scalability and allow us to get more accurate TCO estimates.

A trip to the DMV — and a realization that there had to be a better, more modern way for the system to work — sparked the idea for PayIt, a secure cloud service provider for digital government that launched in 2013. The company’s mission is to help state, local, and government agencies reach their constituents better and more effectively, shifting the reliance from in-office payments to digital ones.

The Grafana Agent team is happy to announce that Grafana Agent 0.14.0-rc2 includes improved Windows support. Up until now, running Grafana Agent — our tool for gathering metrics, logs, and traces — in Windows was difficult and not well supported for Windows best practices. In short, it was not a good Windows citizen. In the new release candidate, we’re making changes to improve the experience, based on feedback from GitHub issues, customer contacts, and our own experience.

When Grafana Labs CEO and co-founder Raj Dutt announced to the team that the company would be relicensing our core open source projects from Apache 2.0 to AGPLv3, he opened the floor for discussion and encouraged anyone who had further questions to reach out. We believe in honesty and transparency, so we collected hard questions from Grafanistas, and Raj answered them for this public Q&A. The time felt right. As I’ve said publicly before, I’ve been thinking about this topic for years.

Grafana Labs was founded in 2014 to build a sustainable business around the open source Grafana project, so that revenue from our commercial offerings could be re-invested in the technology and the community. Since then, we’ve expanded further in the open source world — creating Grafana Loki and Grafana Tempo and contributing heavily to projects such as Graphite, Prometheus, and Cortex — while building the Grafana Cloud and Grafana Enterprise Stack products for customers.