The pressure on today’s development teams is real: innovate, release quickly, and then do it all again, only faster. Is it any surprise that studies are showing 83% of software developers are feeling burnout? We’re here to remind you that it doesn’t have to be this way. 

Honeycomb recently released our Query Assistant, which uses ChatGPT behind the scenes to build queries based on your natural language question. It's pretty cool. While developing this feature, our team (including Tanya Romankova and Craig Atkinson) built tracing in from the start, and used it to get the feature working smoothly.

If debugging has sucked the soul out of your engineers, we’ve got the answer: event-based observability. Instead of spending hours and resources trying to find out why an alert is sounding, event-based observability can quickly surface the correct cause of any issue. Here are five concrete ways teams can save time, money, and sanity using Honeycomb’s event-based approach to observability.

The Honeycomb design team began work on Lattice in early 2021. Over several months, we worked to clean up and optimize typography, color, spacing, and many other product experience areas. We conducted an extensive audit of all components, documenting design inconsistencies and laying the foundation for a sustainable design system. However, a more extensive evaluation and audit were necessary before updating or developing components.

Also known as confidence testing, smoke testing is intended to focus on some critical aspects of the software that are required as a baseline. The term originates in electronic hardware testing; as my colleague Robb Kidd stated, “We want to apply power and see if smoke comes out.” If a smoke test fails, there is almost definitely a problem to address. If it passes, it doesn’t necessarily mean there is no problem, but we can feel confident that the major functionality is okay.

A while ago, we added Metrics to our observability platform so teams could easily see system information right next to their application observability data—no tool or team switching required. So how can teams get the most out of metrics in an observability platform? We’re glad you asked! We had this conversation with experts at Heroku. They’ve successfully blended metrics and observability and understand what is most helpful to know. Here are three strategies to maximize the benefits of Honeycomb Metrics.

Dear Miss O11y, I remember reading quite interesting opinions from you about usage of metrics and traces in an application. Did you elaborate on those points in a blog post somewhere, so I can read your arguments to forge some advice for myself? I must admit that I was quite puzzled by your stance regarding the (un)usefulness of metrics compared to traces in apps in some contexts (debugging).

It’s 5:00 pm on a Friday. You’re wrapping up work, ready to head into the weekend, when one of your high-value customers Slacks you that something’s not right. Requests to their service are randomly timing out and nobody can figure out what’s causing it, so they’re looking to your team for help. You sigh as you know it’s one of those all-hands-on-deck situations, so you dig out your phone and type the "going to miss dinner" text. The team places an order for takeout (a small perk of staying late: expensing dinner) and digs into what’s happening. 

Engineers know best. No machine or tool will ever match the context and capacity that engineers have to make judgment calls about what a system should or shouldn’t do. We built Honeycomb to augment human intuition, not replace it.

Heatmaps are a beautiful thing. So are charts. Even better is that sometimes, they end up producing unintentional—or intentional, in the case of our happy o11ydays experiment—art. 

The modern standard for observability in backend systems is: distributed traces with OpenTelemetry, plus dynamic aggregations over these events. This works very well in the world of web servers. But what about the web client?