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. 

You know that old adage about not seeing the forest for the trees? In our Authors’ Cut series, we’ve been looking at the trees that make up the observability forest—among them, CI/CD pipelines, Service Level Objectives, and the Core Analysis Loop. Today, I’d like to step back and take a look at how observability fits into the broader technical and cultural shifts in technology: cloud-native, DevOps, and SRE.

How do you solve the people and culture problems that are necessary in making the shift to adopt observability practices? And once you instill those changes, how do measure the benefits?

Just like any aspect of development, poor integration, invisible bottlenecks, and bugs can plague your CI/CD pipelines. And debugging them? Well, it’s complicated. To see what I mean, just fire up your favorite search engine to find content on “debugging CI/CD pipelines.” Look at all the different tools, studios, and blogs full of advice—it’s soul crushing.

SLOs—or Service Level Objectives—can be pretty powerful. They provide a safety net that helps teams identify and fix issues before they reach unacceptable levels and degrade the user experience.

But SLOs can also be intimidating. Here’s how a lot of teams feel about them: We know we want SLOs, we’re not sure how to really use them, and we don’t know how to debug SLO-based alerts.

Observability is about understanding systems, which means more than just production. Moving from logs to tracing and showing causality can be done locally, as well. We can give developers the same superpowers that SREs have: observability-driven development. 

In this blog, we’ll dive into debugging with the Core Analysis Loop, the functional requirements for a backend datastore that make this possible, and whether it makes sense to build or buy your own observability solution.

In this webinar based on our O’Reilly book, we go over the difference between observability and monitoring – and when to use which.

Our Author’s Cut series takes a deep dive into our O’Reilly Observability Engineering book. Join us for this session on structured events.

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.

The book is the first to concretely demonstrate the theory and practice of observability in order to provide readers with a blueprint for increasing business velocity SAN FRANCISCO, May 10, 2022 – Honeycomb, the…

The book is done. The book is FINISHED. There will be no more writing of the book! Holy macaroni. If you think you might be sensing just a SMIDGE of relief, you wouldn’t be…

There’s been a fair bit of buzz lately about OpenTelemetry, which is the next major version of the OpenTracing and OpenCensus projects. The leadership of those two projects have come together to create OpenTelemetry,…

This post continues our long-running dogfooding series from How Honeycomb Uses Honeycomb Part 8: A Bee’s Life. To understand how Honeycomb uses Honeycomb at a high level, check out our dogfooding blog posts first — they do…

This post continues our dogfooding series from How Honeycomb Uses Honeycomb, Part 5: The Correlations Are Not What They Seem. In a recent blog post, I talked about what sorts of things should go…