As a Grafanista, you tend to find things to visualize — databases, microservices, classic video games, etc. It’s part of our “big tent” philosophy. So when our December hackathon rolled around, some of us in our internal homelab Slack channel decided to take a look at how we could get metrics out of our Plex Media Servers.
Grafana k6 v0.44.0 has been released, featuring new experimental modules, an upgraded browser module, and tons of improvements. Get Grafana k6 0.44.0 Here’s a quick overview of the latest k6 news from the team and the community.
Grafana 9.5 has arrived! 🔥🎉 Get Grafana 9.5 The latest Grafana release introduces new features and improvements, such as major Grafana Alerting improvements, dashboard and visualization enhancements, a redesigned navigation experience, support bundles for faster issue resolution, and much more to provide you with better insights into your data.
When you sign into the Roblox platform, you get 30 million immersive experiences, ranging from concerts to fashion shows to, of course, video games. But when the observability team at Roblox logs on, they’re not playing around. The Roblox observability engineers are responsible for keeping more than 214 million monthly users happy and engaged by making the wildly popular gaming platform highly available around the world.
IoT devices open the door to all sorts of computing potential, but they can also produce a flood of telemetry data that users need to properly collect and monitor to ensure those devices are working properly. It’s no wonder so many individuals and businesses use Grafana for IoT use cases, whether they’re starting an aquaponic farm in South Africa, managing an industrial-scale electroplating factory in Ohio, or simply keeping tabs on Pretzel the python at its home in the UK.
With countless observability tools, data sources, and environments to juggle, the organizations that deploy and manage today’s distributed applications often face an uphill battle to gain visibility into their application performance. That was a key takeaway from the Grafana Labs Observability Survey 2023, which incorporated input from more than 250 industry practitioners who are all too familiar with these complexities.
Memory leaks can be a significant issue in any programming language, and Go is no exception. Despite being a garbage-collected language, Go is still susceptible to memory leaks, which can lead to performance degradation and cause your operating system to run out of memory. To defend itself, the Linux operating system implements an Out-of-Memory (OOM) killer that identifies and terminates processes that consume too much memory and cause the system to become unresponsive.
Grafana Labs is excited to announce that Grafana Cloud is now available in AWS Marketplace. With this new offering, existing AWS customers can procure, deploy, and scale the fully managed Grafana LGTM observability stack (Loki for logs, Grafana for visualization, Tempo for traces, Mimir for Prometheus metrics) with just a few clicks.
Grafana Alerting enables users to create and customize alert rules as separate entities and link them to Grafana panels. It also supports various data sources with built-in alerting engines, such as Prometheus, Grafana Mimir, and Grafana Loki, allowing users to manage their alert rules directly from Grafana’s UI.
Logging in Kubernetes can help you track the health of your cluster and its applications. Logs can be used to identify and debug any issues that occur. Logging can also be used to gain insights into application and system performance. Moreover, collecting and analyzing application and cluster logs can help identify bottlenecks and optimize your deployment for better performance.
A database is one of the most critical components for almost every application. Making sure it is running with the expected read and write latencies is paramount. This can be the difference between a smooth, pleasing user experience and a slow, error-filled one that makes your customers turn their back on a product — and never come back.
Apache Cassandra is a highly scalable, open source NoSQL database system designed to handle large amounts of data across multiple commodity servers with no single point of failure. Apache Cassandra can be run as a single node but starts making sense when its run in a cluster setup. The system is optimized for high write throughput and is known for its ability to handle big data workloads with ease at super-low latencies.
As more organizations adopt cloud-based services like Microsoft Azure Kubernetes Service (AKS), it becomes increasingly important to monitor and manage the performance and reliability of these services. If you’re using AKS today, then Grafana Cloud provides the flexibility, performance, and visualizations you need to monitor your distributed applications.
Grafana Loki 2.8 is here — and it’s at least 0.1 better than Loki 2.7! Jokes aside, this release includes a number of improvements users will appreciate. In addition to graduating our TSDB index from Experimental to General Availability, we’ve added a number of nifty LogQL features, and we’ve made the Loki deployment and management experience much easier. This also marks the release of Grafana Enterprise Logs (GEL) 1.7.
We often see questions about how to template alerts. In Grafana, you can template information about your alerts with custom labels and annotations, and you can also template how notifications look and what information they contain with notification templates. Many users confuse the two, despite being separate features with different use cases.
As a developer, I’m always thinking about ways I can make my work on projects better, faster, and more organized. Over the years, I’ve learned that tracking development metrics is a really helpful way to do that.
There are many aspects of application performance, but they broadly fall into two categories: frontend performance and backend performance. As a tester, it’s important to know the differences between the two and how that impacts the way you approach your tests. In this blog, I’ll provide a high-level overview of frontend performance testing and backend performance testing, including pros and cons of each one.
