Operations | Monitoring | ITSM | DevOps | Cloud

Over the past six months, we have been working on optimizing query performance in Grafana Mimir, the open source TSDB for long-term metrics storage. First, we tackled most of the out-of-memory errors in the Mimir store-gateway component by streaming results, as we discussed in a previous blog post. We also wrote about how we eliminated mmap from the store-gateway and as a result, health check timeouts largely disappeared.

Victor Sonck is a Developer Advocate for ClearML, an open source platform for Machine Learning Operations (MLOps). MLOps platforms facilitate the deployment and management of machine learning models in production. As most machine learning engineers can attest, ML model serving in production is hard. But one way to make it easier is to connect your model serving engine with the rest of your MLOps stack, and then use Grafana to monitor model predictions and speed.

Each year, more than 296 million packages are shipped around the world via DHL and their premium service, Time Definite International. And at DHL Express Switzerland, a local unit of the international logistics and shipping company, the IT team provides solutions for tracking customs clearance progress, analytics, mobile and optical character recognition (OCR) scanning, and warehouse management on every package that moves through Switzerland.

I’ve had a swimming pool at my house in Massachusetts since 2016. One of the problems that pool owners like myself face when we go on vacation or leave for several days is evaporation from the pool and the water level dropping below the skimmers. This can happen due to sunlight and warm temperatures. It can also happen when temperatures drop at night and the pool is being heated — the water temperature is warmer than the air, causing the water to evaporate.

Christophe is a self-taught engineer from France who specializes in site reliability engineering. He spends most of his time building systems with open-source technologies. In his free time, Christophe enjoys traveling and discovering new cultures, but he would also settle for a good book by the pool with a lemon sorbet.

Grafana k6 is a powerful, developer-friendly tool designed and engineered with a focus on load testing — but it boasts capabilities that extend far beyond that use case. Understanding the inner workings of k6 is helpful to fully leverage its potential, and to tailor the tool to your specific testing needs. Read on to learn how k6 is structured, and how its underlying design provides the best possible reliability and load testing experience.

Observability has traditionally been conceptualized in terms of three core facets: logs, metrics, and traces. For years, these elements have been seen as the “pillars” of observability, serving as the foundational components for system monitoring and delivering key insights to improve system performance. However, with the exponential growth in system complexity, a more comprehensive and unified perspective on observability has become necessary.

At Grafana Labs, we are constantly improving our feature set, and tracing is no different. Traces are often overshadowed by logs and metrics, but they’re a pillar of observability for a reason. Used correctly, organizations that can quickly and successfully follow a chain of events through a system gain a more holistic view of their systems and are better equipped to find and fix issues faster.

One of the most talked about topics in observability today is centered around the question of how to get more value out of the ever-increasing amount of data collected by agents, collectors, scrapers, and the like. Back in May, we announced Adaptive Metrics, a new feature in Grafana Cloud that allows you to reduce the cardinality of Prometheus metrics and the overall volume and costs of your metrics.

With Grafana 10, the latest major release of our data visualization platform, we wanted to explore new ways to empower our developer community. Case in point: Grafana Scenes, a new frontend library that enables developers to create dashboard-like experiences — such as querying and transformations, dynamic panel rendering, and time ranges — directly within their Grafana application plugins.