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.

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 logs vs traces.

About a year ago, Honeycomb kicked off an internal experiment to structure how we do incident response. We looked at the usual severity-based approach (usually using a SEV scale), but decided to adopt an approach based on types, aiming to better play the role of quick definitions for multiple departments put together. This post is a short report on our experience doing it.

Earlier this year, we introduced relational fields. Relational fields enable you to query spans based on their relationship to one other within a trace, rather than only in isolation. We’ve now expanded this feature and introduced four new prefixes: child., none., any2., and any3.. Previously, you could use root., parent., and any. to query on the root span of your target span’s trace, the parent span of your target span, and any other span in the same trace as your target span.

Since its inception in 2004, Lansweeper has been at the forefront of helping businesses understand, manage, and protect their IT devices and networks through a powerful IT asset management platform. As the platform grew from an on-premises solution to a cloud-based SaaS offering, Lansweeper expanded its reach to a global, multi-region customer base.

Observability means you know what’s happening in your software systems, because they tell you. They tell you with telemetry: data emitted just for the people developing and operating the software. You already have telemetry–every log is a data point about something that happened. Structured logs or trace spans are even better, containing many pieces of data correlated in the same record. But you want to start from what you have, then improve it as you improve the software.

As discussed in the first article in this series, a Center of Production Excellence (CoPE) is a more or less formal, provisional subsystem within an organization. Its purpose is to act from within to change that organization so that it’s more capable of achieving production excellence. The series has, to date, focused mainly on how best to construct such a subsystem and what activities it should pursue.

Let’s be real, we’ve never been huge fans of conventional unstructured logs at Honeycomb. From the very start, we’ve emitted from our own codestructured wide events and distributed traces with well-formed schemas. Fortunately (because it avoids reinventing the wheel) and unfortunately (because it doesn’t adhere to our standards for observability) for us, not all the software we run is written by us.

At Honeycomb, we know how important it is for organizations to have a unified observability platform. This is why we’re launching Honeycomb Telemetry Pipeline and Honeycomb for Log Analytics: to enable engineering teams to send and analyze data—including logs—into a single, unified platform. For too long, teams have had to wrangle large volumes of logs, their context scattered across multiple teams and tools, leading to knowledge silos.

Over the past six weeks, we introduced a series of impactful updates aimed at making your observability workflows faster, more unified, and more collaborative. Here’s a snapshot of what we worked on.