All the conditions necessary to alter the career paths of brand new software engineers coalesced—extreme layoffs and hiring freezes in tech danced with the irreversible introduction of ChatGPT and GitHub Copilot. Recession and AI-assisted programming signaled the potential end of a dream to bootcamp-educated juniors. 

Have you ever had an alert go off that you immediately ignore? It’s a nuisance alert—not actionable—but you keep it around just in case. Or maybe you’ve looked at a trace waterfall and wondered what exactly happened during a gap that just doesn’t drill down deep enough to explain what’s going on. Do you know the feeling where you have just enough information to monitor what’s going on in your systems, but not quite enough to put your mind at ease?

Honeycomb engineers were amongst the earliest adopters of this technology. Not in the widely parodied top-down, VP-mandated, “go be AI leaders nao plz” kind of way, but in a bottoms-up, experimental kind of way, driven by curiosity and fascination.

Refinery is a powerful tail-based sampler—but with great power comes great challenges. We heard your feedback and are excited to announce the release of Refinery 2.9, a rather large update that is packed with goodies to make your life easier when running Refinery in your network.

Develocity, formerly known as Gradle Enterprise, is a powerful tool that speeds up local and CI build time, helps troubleshoot your builds, and analyzes your data. At Pinterest, we have a dedicated team, Mobile Builds, and we ensure that developers can build fast and often. This enables developers to be more productive by getting faster feedback on their code.

We’re always interested in improving the signal-to-noise ratio of our internal telemetry at Honeycomb. In an effort to reduce the amount of noise in our logs, we looked at reducing and deduplicating the logs emitted by our infrastructure and applications.

As the company experienced rapid growth, Duolingo remained steadfast in their commitment to delivering a high-quality user experience. This dedication led to the launch of a reliability initiative, which included the formation of a specialized team focused on observability. The engineering team recognized that comprehensive observability was critical to their mission. 

So, how do we get JSON logs into a backend analysis system like Honeycomb that primarily accepts OTLP data? In this post, we’ll cover how to use the filelog receiver component in the OpenTelemetry Collector to parse JSON log lines from logs files, as there are a few ways to achieve this.

OneFootball recognized that observability was essential to delivering a seamless experience—and as seasoned engineers, they prioritized having the right tool to achieve it. Identifying issues quickly, and resolving them before they impacted fans, required visibility across their entire platform. This mission led them to Honeycomb, setting the stage for a transformative journey in how they approach reliability and performance at scale.

With more and often smaller processes, cloud-native architectures have driven the need for better insights into our software—a way to look into how these processes fit together. To accomplish this insight, we use an approach that goes beyond traditional monitoring and provides deep insights into system behavior. This approach is cloud observability.

We’ve been talking about observability 2.0 a lot lately; what it means for telemetry and instrumentation, its practices and sociotechnical implications, and the dramatically different shape of its cost model. With all of these details swimming about, I’m afraid we’re already starting to lose sight of what matters. The distinction between observability 1.0 and observability 2.0 is not a laundry list, it’s not marketing speak, and it’s not that complicated or hard to understand. The distinction is a technical one, and it’s actually quite simple.

In the software space, we spend a lot of time defining the terminology that describes our roles, implementations, and ways of working. These terms help us share fundamental concepts that improve our software and let us better manage our software solutions. To optimize your software solutions and help you implement system observability, this blog post will share the key differences between two important terms: traces and logs.