When Grafana Labs CEO and co-founder Raj Dutt announced to the team that the company would be launching Grafana Mimir, we knew that along with the public announcement, we would want to publish a Q&A. We collected questions from Grafanistas and Raj has answered them here.

Today we’re introducing you to Grafana Mimir, the most scalable, most performant open source time series database in the world. Mimir allows you to scale to 1 billion metrics and beyond, with simplified deployment, high availability, multi-tenancy, durable storage, and blazing fast query performance that is up to 40x faster than Cortex. There’s supposed to be a video here, but for some reason there isn’t. Either we entered the id wrong (oops!), or Vimeo is down.

Grafana Labs and New Relic have a long history of working together to drive cross-functionality so joint customers can benefit from using Grafana and New Relic together. The New Relic data source plugin for Grafana — which is available to users with a Grafana Cloud account or with a Grafana Enterprise license — is no exception. In this quick tutorial video, we’ll not only show you how easy it is to configure the New Relic data source plugin in Grafana.

This week we have turned on full trace retention search (beta) in Grafana Cloud Traces. This feature was also introduced in the recent release of Grafana Tempo v1.3. Previously, if you brought up your Grafana Cloud Traces data source, you were greeted with this message: This message simply warned the user that the Grafana Tempo search was calibrated for recent traces only, regardless of the selected time window.

Managing your on-call rotations just got a little less stressful. With Grafana 8.0, we introduced unified alerting, which centralizes alerting information into a single, searchable view. With the introduction of Grafana OnCall, an easy-to-use on-call management tool available in Grafana Cloud, you can now extend the alerting workflow in Grafana to ensure that the right notifications reach the right people at the right time using the right method.

Relabeling is a powerful tool that allows you to classify and filter Prometheus targets and metrics by rewriting their label set. The purpose of this post is to explain the value of Prometheus’ relabel_config block, the different places where it can be found, and its usefulness in taming Prometheus metrics. Much of the content here also applies to Grafana Agent users. For reference, here’s our guide to Reducing Prometheus metrics usage with relabeling.

Today I’m back with another integration available for Grafana Cloud, our observability platform that gathers all your metrics, logs, and traces under a single roof with Grafana. I’m going to highlight how you can use Grafana with Gitea, an open source forge software package for hosting software development version control. It uses Git as well as other collaborative features like bug tracking, wikis, and code review. It is a great choice for those who manage Git repositories.

Amon Reich is the founder of SmartLiving.Rocks based out of Schweinfurt, Germany, an IoT solutions provider for smart homes and small businesses. Amon maintains the open source SeniorenSmarthome project, which enables Ambient Assisted Living through Grafana dashboards and other open source technologies. I’ve been working in the field of smart technology for over 10 years.

Almost every company who sets up Grafana as part of an observability or data visualization service has multiple teams, divisions, or customers of their own to serve.

We have always thought of our organization as special. Our plans were never to build a traditional business, and we know we have a unique culture. But it is nice when others outside of our company recognize that Grafana Labs is something special, too. This week, we were excited to be included on two very prestigious lists: The Enterprise Tech 30 and America’s Best Startup Employers.

Ever wonder what your application looks like from the “outside in”? Synthetic monitoring can give you a global overview of your application from your customer’s point of view, observing how systems and applications are performing by simulating the user experience. One tool to help achieve this is the Synthetic Monitoring app, which is a blackbox monitoring solution available in Grafana Cloud. You can use Synthetic Monitoring to monitor your services from all over the world.

Here at Grafana Labs, when we’re building integrations for Grafana Cloud, we’re often thinking about how to help users get started on their observability journey. We like to focus some of our attention on the different technologies you might come across along the way. That way, we can share our tips on the best ways to interact with them while you’re using Grafana Labs products.

Today, I am excited to introduce the NSQ integration available for Grafana Cloud, our platform that brings together all your metrics, logs, and traces with Grafana for full-stack observability. NSQ is a real-time distributed messaging platform designed to operate at scale, handling billions of messages per day. It’s a simple and lightweight alternative to other message queues such as Kafka, RabbitMQ, or ActiveMQ. This will walk you through how to get the most out of the integration.

Ever since a JPEG created by the digital artist Beeple sold for more than $69 million in 2021, the worldwide obsession with NFTs (non-fungible tokens) that represent digital collectibles, art, and media has been growing. A company at the forefront of the NFT world is the blockchain gaming studio Dapper Labs, which leverages blockchain to build addictive games (such as CryptoKitties), verify authentic digital collectibles, and run fan tokens for sports personalities and music artists.

In the freshly released Grafana 8.4, we’ve enabled the full-range log volume histogram for the Grafana Loki data source by default. Previously, the histogram would only show the values over whatever time range the first 1,000 returned lines fell within. Now those using Explore to query Grafana Loki will see a histogram that reflects the distribution of log lines over their selected time range.

A summary is a metric type in Prometheus that can be used to monitor latencies (or other distributions like request sizes). For example, when you monitor a REST endpoint you can use a summary and configure it to provide the 95th percentile of the latency. If that percentile is 120ms that means that 95% of the calls were faster than 120ms, and 5% were slower. Summary metrics are implemented in the Prometheus client libraries, like client_golang or client_java.