Observability is important to understand what’s happening in production. But carving out the time to add instrumentation to a codebase is daunting, and often treated as a separate task to writing features. This means that we end up instrumenting for observability long after a feature has shipped, usually when there’s a problem with it and we’ve lost all context. What if we instead treated observability similarly to how we treat tests?
Ever since we launched Query Assistant last June, we’ve learned a lot about working with—and improving—Large Language Models (LLMs) in production with Honeycomb. Today, we’re sharing those techniques so that you can use them to achieve better outputs from your own LLM applications. The techniques in this blog are a new Honeycomb use case. You can use them today. For free. With Honeycomb.
A lot of reasoning in content is predicated on the audience being in a modern, psychologically safe, agile sort of environment. It’s aspirational, so folks who aren’t in those environments may feel like the path there includes doing “the new thing” or using “the new tool.” If you write software and your employer hasn’t caught up to all the newest, best ways to work, I hope this pragmatic post helps you sleep better at night.
Software systems are increasingly complex. Applications can no longer simply be understood by examining their source code or relying on traditional monitoring methods. The interplay of distributed architectures, microservices, cloud-native environments, and massive data flows requires an increasingly critical approach: observability.
As someone living the Honeycomb ops life for a while, SLOs have been the bread and butter of our most critical and useful alerting. However, they had severe, long-standing limitations. In this post, I will describe these limitations, and how our brand new feature, budget rate alerts, addresses them. We usually don’t have SREs writing product announcements, but I’m so excited about this one that I said, “Screw it, I’m doing it!”
Stop me if you’ve heard this one before: you just pushed and deployed your latest change to production, and it’s rolling out to your Kubernetes cluster. You sip your coffee as you wrap up some documentation when a ping in the ops channel catches your eye—a sales engineer is complaining that the demo environment is slow. Probably nothing to worry about, not like your changes had anything to do with that… but, minutes later, more alerts start to fire off.
In telemetry jargon, a pipeline is a directed acyclic graph (DAG) of nodes that carry emitted signals from an application to a backend. In an OpenTelemetry Collector, a pipeline is a set of receivers that collect signals, runs them through processors, and then emits them through configured exporters. This blog post hopes to simplify both types of pipelines by using an OpenTelemetry extension called the Headers Setter.
In our continuous journey to support teams grappling with the complexities of Kubernetes environments, we’re thrilled to announce the launch of Honeycomb for Kubernetes, a dedicated solution designed to bridge the growing divide between infrastructure/platform teams and application developers. This is available to all plans (including Free!) at no additional cost.
