Insightful proof-of-concepts with a tool can be difficult to undertake due to the demands on valuable resources: time, energy, and people. With a task as grand as observability, how could one truly test if Honeycomb and OpenTelemetry are right for their organization and meet their requirements? For this thought experiment, here’s a comprehensive description of the ideal product evaluation over the course of four weeks, given unlimited resources.

People seem to struggle with the idea that there are no repeat incidents. It is very easy and natural to see two distinct outages, with nearly identical failure modes, impacting the same components, and with no significant action items as repeat incidents. However, when we look at the responses and their variations, we can find key distinctions that shows the incidents as related, but not identical.

OpenTelemetry and Beelines were designed with assumptions about the types of traffic that most users would trace.

At Honeycomb, we are all about observability. In the past, we have proposed observability-driven development as a way to maximize your observability and supercharge your development process. But I have a problem with the terminology, and it is: I don’t want observability to drive your development.

Quickly: if you’re interested in observability for LLMs, we’d love to talk to you! And now for our regularly scheduled content: In early May, we released the first version of our new natural language querying interface, Query Assistant. We also talked a lot about the hard stuff we encountered when building and releasing this feature to all Honeycomb customers. But what we didn’t talk about was how we know how our use of an LLM is doing in production!

The benefits of going cloud-native are far reaching: faster scaling, increased flexibility, and reduced infrastructure costs. According to Gartner®, “by 2027, more than 90% of global organizations will be running containerized applications in production, which is a significant increase from fewer than 40% in 2021.” Yet, while the adoption of containers and Kubernetes is growing, it comes with increased operational complexity, especially around monitoring and visibility.

We saw a shift this year in how the technology sector honed in on sustainability from a cost perspective. In particular, looking at where they’re spending that revenue in the infrastructure and tooling space. Observability tooling comes under a lot of scrutiny as it’s perceived as a large cost center—and one that could be cut without affecting revenue. After all, if the business hasn’t had a problem in the last few months, we mustn’t need monitoring—right?

While Kubernetes comes with a number of benefits, it’s yet another piece of infrastructure that needs to be managed. Here, I’ll talk about three interesting ways that Honeycomb uses Honeycomb to get insight into our Kubernetes clusters. It’s worth calling out that we at Honeycomb use Amazon EKS to manage the control plane of our cluster, so this document will focus on monitoring Kubernetes as a consumer of a managed service.

Kubernetes is the gold standard for container orchestration at scale. While massive global companies like Google, Spotify, and Pinterest rely on Kubernetes to run their software in production, so do many small but mighty developer teams. (Full disclosure: Honeycomb joined the Kubernetes brigade last year, when we migrated some of our services.)

Running a Kubernetes cluster isn’t easy. With all the benefits come complexities and unknowns. In order to truly understand your Kubernetes cluster and all the resources running inside, you need access to the treasure trove of telemetry that Kubernetes provides. With the right tools, you can get access to all the events, logs, and metrics of all the nodes, pods, containers, etc. running in your cluster. So which tool should you choose?

Martin and Jess recently conversed with Todd Gardner of RequestMetrics as part of the O11ycast podcast. We don’t normally write blogs based on these conversations, but there were impactful comments in that episode that bear repeating. You can listen to the full conversation if you wish. Let’s get into it!

In a simple deployment, an application will emit spans, metrics, and logs which will be sent to api.honeycomb.io and show up in charts. This works for small projects and organizations that do not control outbound access from their servers. If your organization has more components, network rules, or requires tail-based sampling, you’ll need to create a telemetry pipeline.