Operations | Monitoring | ITSM | DevOps | Cloud

Even though I’m a Prometheus maintainer and work inside the Prometheus code, I found many of the details of PromQL, the Prometheus query language, obscure. Many times I would look something up, or go deep into the code to find out exactly what it did, only to forget it again the next month. So, trying to live up to my job title of Distinguished Engineer at Grafana Labs, I resolved to write the definitive guide: what really happens when I execute a PromQL query?

In the observability space, ease-of-use has always been a key differentiator for Grafana. As much as we want to offer a powerful observability platform to our users, we also want to ensure they can get up and running as quickly as possible. Still, for those of you sitting down to build your first dashboard, we totally understand that a little guidance can go a long way.

When you investigate an incident, context is everything. Let’s say you’re working on-call and get pinged in the middle of the night. You open the alert and it sends you to a dashboard where you recognize a latency pattern. But is the spike normal for that time of day? Is it even relevant? Next thing you know, you’re expanding the time window and checking other related metrics as you try to figure out what’s going on. It’s not to say you won’t find the answers.

In simple terms, containers are a standard package of software that enable applications to run consistently across different computing environments. Often, these applications are broken down into smaller collections of independent services known as microservices. For many organizations, these microservices-based applications have replaced traditional monolithic applications because they offer increased performance, flexibility, and scale.

When incidents occur, engineers must jump into action to get systems back to running at peak performance. However, there are a myriad of challenges that can prevent them from resolving the issues swiftly. Imagine a scenario where a team of DevOps engineers manages a cloud-based e-commerce platform that experiences occasional spikes in traffic during peak shopping seasons. During one of those major sales events, the team notices a sharp spike in CPU usage across several critical application servers.

Daria Volkova is a Grafana champion and Volkov Labs co-founder. The Business Suite for Grafana is a collection of uniquely positioned plugins developed by Volkov Labs. Each offers flexible and adaptable solutions for a wide range of business needs that go beyond observability, including file uploads, building a chart of any kind and configuration, leveraging all aspects of web design, video streaming, and more. This blog post provides details, examples, and short tutorials.

We consistently roll out helpful updates and fun features in Grafana Cloud, our fully managed observability platform powered by the open source Grafana LGTM Stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). And this month, on the heels of ObservabilityCON 2024 — our flagship observability event — we have no shortage of updates to share.

There are a lot of factors that go into choosing an observability solution. And even after all that careful consideration, sometimes the platform you initially invest in doesn’t meet your needs, especially as your organization grows and evolves. For that very reason, we’ve seen users begin their observability journeys with another tool, and then decide to migrate to Grafana Cloud, our fully managed cloud-hosted observability platform.

Doug Tidwell is the Director of Content at Altinity, responsible for creating useful content for ClickHouse users in general and Altinity customers in particular. He has more than 30 years of experience in databases, CI/CD systems, development tools, and middleware. When it comes to visualizing, monitoring, and logging ClickHouse clusters, there’s no easier way to accomplish all three than with Grafana Cloud, the open and composable observability stack powered by open source.

The OpenTelemetry (OTel) project offers numerous components and instrumentations that support different languages and telemetry signals. However, this flexibility can be overwhelming, and new users often struggle to choose the right components and configure them properly for their specific use cases. To address this, OpenTelemetry defines the concept of a distribution, a tailored and customized version of OpenTelemetry components.