Operations | Monitoring | ITSM | DevOps | Cloud

Today, I found a bug before I noticed it. Like, it was subtle, and so I wasn’t quite sure I saw it—maybe I hadn’t hit refresh yet? Later, I looked at the trace of my function and, boom, there was a clear bug. Here’s the function with the bug. It responds to a request to /win by saving a record of the win and returning the total of my winnings so far. Can you spot the problem in the TypeScript? It’s subtle. Now here’s a trace in Honeycomb: Now do you see the bug?

Development is no longer a linear journey from point A to point B. As more projects shift into a state of organic growth, user feedback and constant experimentation are increasingly becoming the norm, if not the standard for engineering. “In order to support this rapid experimentation, we’re beginning to embrace new working methods and practices,” said Vinodh Ravi, Executive Director of Platform Engineering at JPMorgan Chase.

I have a good sense of how to use traces to understand my system’s behavior within request/response cycles. What about multi-request processes? What about async tasks spawned within a request? Is there a higher-level or more holistic approach?

With Kubernetes emerging as a strong choice for container orchestration for many organizations, monitoring in Kubernetes environments is essential to application performance. Kubernetes allows developers to develop applications using distributed microservices introducing new challenges not present with traditional monolithic environments. Understanding your microservices environment requires understanding how requests traverse between different layers of the stack and across multiple services.

One of the most common questions we get at Honeycomb is “What insights can you get in the browser?” Browser-based code has become orders of magnitude more complex than it used to be. There are many different patterns, and, with the rise of Single Page App frameworks, a lot of the code that is traditionally done in a backend or middle layer is now being pushed up to the browser. Instead, the questions should be: What insights do frontend engineers want?

OpenTelemetry offers vendor-agnostic APIs, software development kits (SDKs), agents, and other tools for collecting telemetry data from cloud-native applications and their supporting infrastructure to understand their performance and health.

Today we are introducing Local Tail-Based sampling in Kamon Telemetry! We are going to tell you all about it in a little bit but before that, let’s take a couple minutes to explore what is sampling, how it is used nowadays, and what motivated us to including local tail sampling in Kamon Telemetry.

Today, we’re thrilled to announce the early access of our Service Performance Monitoring capability. As today’s DevOps teams know all too well, monitoring application requests in modern microservices architectures is extremely difficult. Requests typically travel across a vast ecosystem of microservices and, as a result, it is often a significant challenge to pinpoint a specific failure in one of these underlying services.