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Lex Neva
We recently took a daring step to test and improve the reliability of the Honeycomb service: we abruptly destroyed one third of the infrastructure in our production environment using AWS’s Fault Injection Service. You might be wondering why the heck we did something so drastic. In this post, we’ll go over why we did it and how we made sure that it wouldn’t impact our service.
Nick Travaglini
Getting the right people working in the CoPE is crucial to success because these change agents must limber up the organization and promote the flexibility necessary to perform resilience.
Liz Fong-Jones
Two years ago, we shared our experiences with adopting AWS Graviton3 and our enthusiasm for the future of AWS Graviton and Arm. Once again, we’re privileged to share our experiences as a launch customer of the Amazon EC2 R8g instances powered by AWS Graviton4, the newest generation of AWS Graviton processors.
Tyler Wilson
Generative AI is having a bit of a moment—well, maybe more than just a bit. It’s an exciting time to be alive for a lot of people. But what if you see stories detailing a six month old AI firm with no revenue seeking a $2 billion valuation and feel something other than excitement in the pit of your stomach?
Rox Williams
In the not-too-distant past, building software was relatively straightforward. The simplicity of LAMP stacks, Rails, and other well-defined web frameworks provided a stable foundation. Issues were isolated, systems failed in predictable ways, and engineers had time to innovate on new features for the business. And it was good.
Martin Thwaites
Having telemetry is all well and good—amazing, in fact. It’s easy to do: add some OpenTelemetry auto-instrumentation libraries to your stack and they’ll fill your disks with data pretty quickly. However, having good telemetry data—data that’s curated into being useful—is something that is both cost-effective and represents good value.
Everyone’s talking about “observability,” but many don’t know what it is, what it’s for, or what benefits it offers. With this framing of observability in terms of goals instead of tools, we hope teams will have better language for improving what their organization delivers and how they deliver it.
Terra Field
Earlier this year, we upgraded from Confluent Platform 7.0.10 to 7.6.0. While the upgrade went smoothly, there was one thing that was different from previous upgrades: due to changes in the metadata format for Confluent’s Tiered Storage feature, all of our tiered storage metadata files had to be converted to a newer format.
In part one of our CoPE series, we analogized the CoPE with safety departments. David Woods says that those safety departments must be: independent, involved, informed, informative. In this post, we’ll elaborate on what each of those characteristics means, why the CoPE should also match those qualifications, and how to achieve that status.
Hazel Edmands
Our storage engine, affectionately known as Retriever, has served us faithfully since the earliest days of Honeycomb. It’s a tool that writes data to disk and reads it back in a way that’s optimized for the time series-based queries our UI and API makes. As usage of this feature has grown, however, we’ve noticed Retriever creaking in novel ways, pushing us to reconsider a core architectural choice.
Jessica Nunn
Earlier this year, Honeycomb announced the launch of data residency in Europe. To meet the growing needs of our customers in the region, we are delighted to announce new Honeycomb Support business hours.
Software is in a crisis. This is nothing new. Complex distributed systems are perpetually in a state far from equilibrium, operating in what Richard Cook has called a “degraded mode.” It’s through a combination of technical artifacts, organizational practices and policies, and pure gumption that they manage to maintain themselves through time.
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Charity Majors
In my February 2024 piece The Cost Crisis in Observability Tooling, I explained why the cost of tools built atop the three pillars of metrics, logs, and traces—observability 1.0 tooling—is not only soaring at a rate many times higher than your traffic increases, but has also become radically disconnected from the value those tools can deliver. Too often, as costs go up, the value you derive from these tools declines.
Over the past five years, software and systems have become increasingly complex and challenging for teams to understand. Simply understanding what’s broken is difficult enough, but trying to do so while balancing the need to constantly innovate and ship makes the problem worse. Your end users have options, and if your software systems are unreliable, they’ll choose a different one.
Winston Hearn
Recently, Honeycomb released a Web Instrumentation package built around the OpenTelemetry browser JS packages. In this post, I’ll go over what the OpenTelemetry auto-instrumentation package gives you, and what Honeycomb’s distribution adds in order to give you even more insight into your web services.
Josephine Yuan
We recently introduced relational fields, a new feature that allows you to query spans based on their relationship to each other within a trace. This post identifies use cases that were previously impossible (or extremely difficult!) without these relational fields.
Honeycomb for Frontend Observability gives frontend developers the ability to quickly identify opportunities for optimization within their web app. This starts with better OpenTelemetry instrumentation, available as an NPM package, that lets you instrument and collect attribution data on Core Web Vitals in under an hour.
Aiden Senner
The 1981 book Simulacra and Simulation by Jean Baudrillard is widely read and cited within academic circles but also permeates popular culture, influencing films, literature, and art. His theories notably influenced the Wachowski siblings’ The Matrix series, bringing some of his ideas into mainstream awareness.
Expanded fields allow you to more easily find interesting traces and learn about the spans within them, saving time for debugging and enabling more curiosity within your team around how transactions perform throughout your services.
Austin Parker
You’re probably familiar with the concept of real user monitoring (RUM) and how it’s used to monitor websites or mobile applications. If not, here’s the short version: RUM requires telemetry data, which is generated by an SDK that you import into your web or mobile application. These SDKs then hook into the JS runtime, the browser itself, or various system APIs in order to measure performance. These SDKs are usually pretty optimized for both speed and size—you don’t want the dependency that tells you how fast or slow your application is to impact your application speed, after all.
