In January 2022, Honeycomb kicked off a one year experiment to have an employee sit as a voting board member on the board of directors. I became that board member in July 2022. As the experiment comes to a close, I want to share some of my reflections.

With one key practice, it’s possible to help your engineers sleep more, reduce friction between engineering and management, and simplify your monitoring to save money. No, really. We’re here to make the case that setting service level objectives (SLOs) is the game changer your team has been looking for.

Honeycomb’s Service Map gives you the unique capability to filter by both services and traces so you can generate maps that isolate very specific areas of your environment. This helps you surface issues quickly and gain a granular understanding of how everything in your system works and evolves over time. And now that a Service Map scenario is available in our Sandbox, you can see it for yourself.

When an organization signs up for Honeycomb at the Enterprise account level, part of their support package is an assigned Technical Customer Success Manager. As one of these TCSMs, part of my responsibilities is helping a central observability team develop a strategy to help their colleagues learn how to make use of the product. At a minimum, this means making sure that they can log in, that relevant data is available, that they receive training on how to query, and perhaps that they collaborate with the rest of Honeycomb’s CS department to solve problems as they arise.

Over the past few months, we've been hard at work modernizing Honeycomb’s data visualizations to address consistency issues, confusing displays, access to settings, and to improve their overall look and feel.

Incidents happen. What matters is how they’re handled. Most organizations have a strategy in place that starts with log searches—and logs/log searching are great, but log searching is also incredibly time consuming. Today, the goal is to get safer software out the door faster, and that means issues need to be discovered and resolved in the most efficient way possible.

The best mechanism to combat proliferation of uncontrolled resources is to use Infrastructure as Code (IaC) to create a common set of things that everyone can get comfortable using and referencing. This doesn’t block the ability to create ad hoc resources when desired—it’s about setting baselines that are available when people want answers to questions they’ve asked in the past.

We sat down with user Zach McCoy, Principal Staff Engineer at Jack Henry, in a recent technical session and learned more about how they are implementing Service Map and BubbleUp to surface issues early and get ahead of customer latency. 

There are limits to what individuals or teams on the ground can do, and while counting fires or their acreage can be useful to know the burden or impact they have, it isn’t a legitimate measure of success. Knowing whether your firefighters or whether your prevention campaigns are useful can’t rely on these high-level observations, because they’ll be drowned in the noise of a messy unpredictable world.

George Miranda, Liz Fong-Jones, and Charity Majors, held a series of live discussions called the Authors’ Cut to bring core concepts of the book to life by applying them to real-world use cases. Now that the series is complete, we thought it would be helpful to combine all of the discussion recaps for your viewing pleasure. Each blog post below takes key concepts from chapters in the book and makes them more digestible. 

We’re here to show you three ways you can jumpstart a distributed tracing effort, starting small and expanding as it makes sense. These examples involve only a little code and perhaps a bit of a mindset change. Starting small with distributed tracing can even be fun, because who doesn’t like getting customized results without much work? 

In this post, we’re moving from the foundations of observability to things that become critical when you start practicing observability at scale. Tools like sampling and telemetry pipelines are useful at any size, but when your trickle of observability data suddenly becomes a torrential flood, these tools are essential.