This blog post was co-authored by Igor Shvartser, Senior Technical Product Manager at Amazon Timestream, and Michael Mandrus, Senior Software Engineer at Grafana Labs. Grafana Labs Senior Software Engineers Stephanie Hingtgen and Kevin Minehart also helped with the content.
Feature management is an emerging set of tools and techniques for developing and testing software based around feature flags. It’s intended to increase productivity and performance, as well as improve software quality. Of course, you’ll also need to keep tabs on all those feature flags, so it only makes sense to pair feature management with observability for a more holistic view of your software development cycles.
I’ve been working with Grafana Tempo for about half a year now, and one thing I like about it is that Tempo requires only object storage for storing traces, which is easy to set up in both cloud environments and on-premises. Another outstanding feature is TraceQL, which allows searching for relevant traces with a powerful query language.
In addition to the built-in user authentication that utilizes usernames and passwords, Grafana also provides support for various mechanisms to authenticate users, so you can securely integrate your instance with external identity providers. We are excited to announce that with the release of Grafana 10.0, we have introduced a new user interface that simplifies the configuration of SAML authentication for your Grafana instances.
In the observability space, it seems like everyone is talking about how to reduce costs and control the explosion of Prometheus metrics. It’s no wonder — our recent analysis of user environments suggests 20% to 50% of metrics generated are never used, but users are still stuck paying for them.
Interest in generative AI and large language models (LLMs) has exploded in popularity thanks to a slew of announcements and product releases, such as Stable Diffusion, Midjourney, OpenAI’s DALL-E, and ChatGPT. The arrival of ChatGPT in particular was a bellwether moment, especially for developers. For the first time, an LLM was readily available and good enough that even non-technical people could use it to generate prose, re-write emails, and generate code in seconds.
We’re excited to introduce a dedicated Grafana Cloud solution for Apache Mesos, an open-source project for managing clusters in your data center and at cloud scale. Apache Mesos is a distributed systems kernel, running on every machine in a cluster and providing easy orchestration of every resource in the cluster. This allows you to treat compute units, memory, and disk as a single pool of resources.
According to the World Bank, two-thirds of adults around the globe currently make or receive digital payments. Businesses have come to expect quick, reliable processing, and one company at the forefront of that is Worldline. The global payment service provider (PSP) is a leading payment processor and payment provider in Europe, with about 3.4 billion e-commerce transactions made in 2022.
Grafana Labs has always been actively involved in the OpenTelemetry community, even working with the predecessor projects OpenTracing and OpenCensus. We have been supporting OTLP as the primary input protocol for our distributed tracing project, Grafana Tempo, since its inception, and our Grafana Agent embeds parts of the OpenTelemetry Collector.
The frontend of a web application is the part that users directly interact with. It’s the last mile of the digital service you deliver to your customers and it’s directly associated with customer satisfaction and business objectives. Knowing performance metrics such as CPU or memory is helpful, but at the end of the day, what you care most about is if the user experience is affected.
Grafana Agent v0.35 is here! The latest release of the Grafana Agent brings with it loads of new features and enhancements. Today, we’ll highlight our work on horizontal scalability and making it simpler than ever to get started using the Agent. Let’s take a look!
In recent months, we have been working on improving the performance of Grafana Mimir, the open source, horizontally scalable, highly available, multi-tenant TSDB for long-term storage of your metrics.
Since Grafana started 10 years ago, there have been more than 43,000 commits to the open source project. Grafana founder Torkel Ödegaard has made more than 7,600 of those commits, and he recently reflected on some personal favorites he’s worked on, ranging from early query builders to the latest navigation updates. Torkel isn’t the only one who has strong feelings.
Anna Kapuścińska is a Software Engineer at Isovalent, who has a rich experience wearing both developer and SRE hats across the industry. Now she works on Isovalent observability products such as Hubble, Tetragon, and Timescape, as well as the respective Grafana integrations for all of them.
Greetings, Kubernetes Time Lords! Through a series of recent updates to our multi-purpose Kubernetes Monitoring solution in Grafana Cloud, we’ve made it easier than ever to assess your resource utilization, whether you’re looking at yesterday, today, or tomorrow. All companies that use Kubernetes, regardless of size, should monitor their available resource utilization. If a fleet is under-provisioned, the performance and availability of applications and services are at serious risk.
Grafana is a rich platform for data visualization, giving you full control over how your data should be visualized. However, this flexibility and freedom comes with some challenges from a security perspective — challenges that need to be solved to protect the data in Grafana. For years, cross-site scripting (XSS) has been among the most common web application security vulnerabilities.
The Prometheus alerting model is a flexible tool in every observability toolkit. When enhanced with Grafana data sources, you can easily alert on any data, anywhere it might live, using the battle-tested label semantics and alerting state machine that Prometheus defines. Often, engineers want to see patterns in their alerts over time, in order to observe trends, make predictions, and even debug alerts that might be firing too often.
Relational databases like MySQL, PostgreSQL, Oracle, and others have a wealth of time series data locked inside of them. Often this data can be used to enhance observability dashboards, or keep track of important application factors, like how many users have signed up for a service. In this article, we’re going to show you how to visualize any time series from any SQL database in Grafana using the time series visualization.
Grafana Mimir is an open source distributed time series database. Publicly launched in March 2022, Mimir has been designed for storing and querying metrics at any scale. Highly available, highly performant, and cost-effective, Mimir is the underlying system powering Grafana Cloud Metrics, and it’s used by a growing open source community that includes individual users, small start-up companies, and large enterprises like OVHcloud.
Today I want to talk about metric queries. More specifically, I want to talk about an important concept that is going to make your queries run faster, give you more accurate results, and make your Grafana Loki operators (like me) much happier. A metric query in Loki looks like this: And the part I want to talk about is that at the end. Now, if you’re like me and have a short attention span and are already bored — I understand.
The Grafana Labs ecosystem is built on a range of different projects that incorporate logs, metrics, traces across load testing, and Kubernetes monitoring. I’ll assume you know all of that data (and more!) can be visualized in Grafana. What made my observability dream become reality, though, is how these systems can work together to help you effectively debug performance issues and operate your system with more confidence.
