Meet the Author
Jessica Kerr (Jessitron)
Engineering Manager of Developer Relations
Jess is a symmathecist, in the medium of code. She sees development teams as learning systems made of people and running software. If we make that software teach us what's happening, it's a better teammate. And if this process makes us into systems thinkers, we can be better persons in the world.
Explore Author's Blog
Tame Your Telemetry: Introducing the Honeycomb Telemetry Pipeline
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.
Honeycomb + Google Gemini
Today at Google Next, Charity Majors demonstrated how to use Honeycomb to find unexpected problems in our generative AI integration. Software components that integrate with AI products like Google’s Gemini are powerful in their ability to surprise us. Nondeterministic behavior means there is no such thing as “fully tested.” Never has there been more of a need for testing in production!
APM From a Developer’s Perspective
In twenty years of software development, I did not have the privilege of being on call, of tending to my software in production. I’ve never understood what “APM” means. Anybody can tell me what it stands for—Application Performance Monitoring (or sometimes, the M means Management)—but what does it mean? What do people use APM for? Now, I work at an observability company—and still, no one can give me a satisfying definition of “APM.” So I did some research, and now the use of APM makes sense from a few angles.
Ship First, Model Later: A Short Recap of AI.Dev
In a keynote at AI.Dev, Robert Nishihara (CEO, Anyscale) described the shift: A year ago, the people working with ML models were ML experts. Now, they’re developers. A year ago, the process was to experiment with building a model, then put a product on top of it. Now, it’s ship a product, find the market fit, then create customized models.
Observability and the DORA metrics
The Accelerate State of Devops Report highlights four key metrics (known as the DORA metrics, for DevOps Research & Assessment) that distinguish high-performing software organizations: deployment frequency, lead time for changes, time-to-restore1, and change…
What Observability-Driven Development Is Not
At Honeycomb, we are all about observability. In the past, we have proposed observability-driven development as a way to maximize your observability and supercharge your development process. But I have a problem with the terminology, and it is: I don’t want observability to drive your development.
Developing with OpenAI and Observability
Honeycomb recently released our Query Assistant, which uses ChatGPT behind the scenes to build queries based on your natural language question. It’s pretty cool. While developing this feature, our team (including Tanya Romankova and Craig Atkinson) built tracing in from the start, and used it to get the feature working smoothly.
Observable Frontends: the State of OpenTelemetry in the Browser
The modern standard for observability in backend systems is: distributed traces with OpenTelemetry, plus dynamic aggregations over these events. This works very well in the world of web servers. But what about the web client?
Ask Miss O11y: Error: missing ‘x-honeycomb-dataset’ header
Your API Key (in the x-honeycomb-team header) tells Honeycomb where to put your data. It specifies a team and an environment. Then, Honeycomb figures out which dataset to put each event in, based on the service.name field in the event. Except…
Caring for Complex Systems: We Can Do This
When we work at it, professionals are pretty good at analysis. We can break down a simple system, look at its parts and their relations, and master it. Given enough time and teammates, we can analyze a very complicated system and fix it when it breaks. But complex systems don’t yield to analysis. We have to add another skill: sense-making.
Iterating on an OpenTelemetry Collector Deployment in Kubernetes
When you want to direct your observability data in a uniform fashion, you want to run an OpenTelemetry collector. If you have a Kubernetes cluster handy, that’s a useful place to run it. Helm is a quick way to get it running in Kubernetes; it encapsulates all the YAML object definitions that you need. OpenTelemetry publishes a Helm chart for the collector.
Ask Miss O11y: How Can I Convince My Organization to Invest in Instrumenting for Observability?
“Dear Miss O11y, I’ve been following Honeycomb for a long time, and I understand where the insights from observability fit in. But larger orgs haven’t experienced this yet. When you’re talking to a C-level executive or director, how do you speak to this? What success stories do you cite that have traction at this level?”
Send a Test Span Through an OpenTelemetry Collector
The scenario: you want to see distributed traces, maybe for your web app. You’ve set up an OpenTelemetry collector to receive OTLP traces in JSON over HTTP, and send those to Honeycomb (how to do that is another post, and we’ll link it here when it’s up). The question: does it work? Let’s test the collector and its connection to Honeycomb by sending a test span.
We Learn Systems by Changing Them
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. Change some text to “HELLO JESS” and see it on the screen. Break something on purpose and see what fails. Refactor and find compilation errors. After I make some real changes and shepherd them into production, then I’m integrated with the code, I come to an understanding of it.