As you add more panels to your dashboard, more requests are being made, potentially leading to your dashboard taking longer to load. While you can limit the data requested in each query, one of the best ways to reduce the loading time is to reduce the number of requests being made to the data source. Grafana makes a data source query for each panel in your dashboard, even if those queries are identical.
Helm charts are great. They combine high quality, ready-made runtime configurations for a huge number of applications with an incredible getting-started experience. There is literally no faster way to install a production-ready Grafana or Loki on Kubernetes than using helm install. Unfortunately, Helm charts can also be incredibly inflexible.
We’ve just launched Cortex 1.4.0, one of the most significant releases of 2020. The big headline: The new blocks storage engine has exited the experimental phase and is now marked as Generally Available. Blocks storage aims to reduce the operational complexity and costs of running a Cortex cluster at scale. In particular, it removes the dependency from a NoSQL database to store series indexes.
In early 2020, the Wavefront team was merged into the newly formed “Tanzu” portfolio under VMware’s Modern Applications business unit.
Often there’s a focus on how a service is running from the perspective of the organization. But what does service health monitoring look like from the perspective of a user? Today, understanding your end users’ experience is a key component of ensuring your website or application is functioning correctly. Having a website that is performing well regardless of location, load, or connection type is no longer a nice-to-have, but rather a requirement.
In collaboration with the AWS team, we have just launched another AWS integration, the X-ray data source. Combined with the CloudWatch and Timestream integrations, the AWS X-Ray data source simplifies monitoring and triaging with one Grafana console. The addition of the AWS X-ray data source reflects Grafana’s commitment to becoming a full observability platform that supports distributed tracing as well as metrics and logs.
Greetings! This is Eldin reporting from the Solutions Engineering team at Grafana Labs. In previous posts, you might have read about announcing ObservabilityCON or our release of Grafana 7.2. In this week’s post, I am introducing Dave Frankel, who will be covering our updated ServiceNow plugin. – Eldin In a previous post we announced the release of our Enterprise ServiceNow plugin. Our first release was focused around incident and change management based on the feedback we received.
Today, AWS launched Amazon Timestream, a fast, scalable, serverless time series database purpose-built for IoT use cases. If you’re looking into trying out Timestream, know that you can visualize the native Timestream queries with Grafana out of the box. Here are some examples of the robust, SQL-style Timestream queries visualized in Grafana.
What range should I use with rate()? That’s not only the title of a true classic among the many useful Robust Perception blog posts; it’s also one of the most frequently asked questions when it comes to PromQL, the Prometheus query language. I made it the main topic of my talk at GrafanaCONline 2020, which I invite you to watch if you haven’t already. Let’s break the good news first: Grafana 7.2, released only last Wednesday, introduced a new variable called $__rate_interval.
My name is Jonathan Stines, and I am a Penetration Tester for Rapid7, a cybersecurity company located in Austin, Texas. A small handful of my former colleagues at Rapid7 now work at Grafana Labs and have said it was a pretty cool spot to have landed. I had a vague understanding of what Grafana was, but what really struck my interest was when I saw their sweet dashboards in the HBO series Silicon Valley.
