In this article, I’m going to walk you through adding attributes to your spans in.NET that contain information about the code that generated the span. We’ll also look at ways to do this automatically using a library I’ve created.
“Lead time to deploy” means the interval from when the code gets written to when it’s been deployed to production. It has also been described as “how long it takes you to run CI/CD.” How important is it? It’s nigh-on impossible to have a high-performing team if you have a long lead time, and shortening your lead time makes your team perform better, both directly and indirectly.
My organization doesn’t want me spending time on instrumenting my product. What can I do? Thanks for the question! You’ll be relieved to hear that you’re in the majority, and also that there are quick (and easy) steps you can do to prove that instrumenting your code is worthwhile.
At Intercom, we focus on customer experience above all—our service’s availability and performance is our top priority. That requires a strong culture of observability across our teams and systems. As a result, we invest a lot in the reliability of our application. But unpredictable failures are inevitable, and when they happen it’s humans that fix them. We operate a socio-technical system, and its ability to recover when faced with adversity is called resilience.
In the old days, the most senior members of an engineering team were the best debuggers. They had built up such an extensive knowledge about their systems that they instinctively knew the right questions to ask and the right places to look. They even wrote detailed runbooks in an attempt to identify and solve every possible issue and possible permutation of an issue.
If you've stumbled (or purposefully landed) on this blog post, chances are you are new to—or diving deeper—into the observability space, o11y for short. Suffice it to say, you’re not in Kansas anymore. Honeycomb in a lot of ways can serve as a yellow brick road into o11y, and this article should serve as an introduction into how Honeycomb facilitates implementing o11y into applications and distributed services.
How do you go from A to Z with observability and OpenTelemetry? This post answers a question we hear often: “How do I get started on instrumentation with OpenTelemetry, while also following best practices for the long-term?” This article is all about taking you from A to Z on instrumentation. This will help you: We will use a simple greeting service application written in Node.js to understand the journey. You can find the pre-instrumented state here.
At Honeycomb, we love involving our customers when we can. We asked: “What was the first thing you found value from with Honeycomb?” Here are some of our favorite answers.
Since 2013, Monitorama has been a community-driven conference, bringing together open source development and operations engineers to focus on pushing the boundaries of monitoring software and practices. It’s chock full of thought-provoking content in the conference talks. The casual atmosphere also makes the hallway track a great way to network with fellow engineers and vendors alike to pick up on new developments in the monitoring space.
A while ago, I wrote about how we track on-call health, and I heard from various people about how “expecting to be woken up” can be extremely unhealthy, or how tracking the number of disruptions would actually be useful. I took that feedback to heart and wanted to address the issues they raised, and also provide some numbers that explain the position I took with these metrics on alerts.
It is only possible to come to an understanding of a system of interest by trying to change it. Here, Jackson contrasts action research with old-style hard science, which tries to study a system from the outside. Laboratories draw a line between experiment and scientist. In the social world, there is no outside: we participate in the systems we study. I’ve noticed this in code: when I come to an existing codebase, I get a handle on it by changing stuff.
