This week we’re shipping a few changes to our Query Builder interface. While they may appear small, they are the first steps in a larger plan that will help you more easily share your own observations about queries with your teammates, all around Honeycomb.
It wasn’t until the conference was ending that we learned that everybody thought we were kinda nuts.
Nobody knows your services/infra better than you, not even Honeycomb. If there’s one maxim for Honeycomb, it’s that context is king. Context determines the questions you can ask. The only way to make complex systems truly tractable is to make all questions possible. Ergo: more context. Context everywhere. Context coming out of the walls. You should be awash in context.
When we released derived columns last year, we already knew they were a powerful way to manipulate and explore data in Honeycomb, but we didn’t realize just how many different ways folks could use them. We use them all the time to improve our perspective when looking at data as we use Honeycomb internally, so we decided to share. So, in this series, Honeycombers share their favorite derived column use cases and explain how to achieve them.
Now Java developers can leverage Honeycomb to gain insight into the behavior of their apps and services by using an SDK similar to the ones we’ve already released for Go, Python, Javascript and Ruby.
In our previous post about Honeycomb Tracing, we used tracing to better understand Honeycomb’s own query path. When doing this kind of investigation, you typically have to go back and forth, zooming out and back in again, between your analytics tool and your tracing tool, often losing context in the process.
When we released derived columns last year, we already knew they were a powerful way to manipulate and explore data in Honeycomb, but we didn’t realize just how many different ways folks could use them. We use them all the time to improve our perspective when looking at data as we use Honeycomb internally, so we decided to share. So, in this series, Honeycombers share their favorite derived column use cases and explain how to achieve them.
We’ve been on a roll this year with Beelines, our integrations for quick, easy, and automagic instrumentation of your apps. You may have already seen our Node.js, Ruby, and Go beelines – today, we’re excited to announce the release of the Honeycomb Beeline for Python!
We’re all collectively trying to define observability (“o11y,” pronounced “olly”) these days, and, as Honeycomb is sometimes described as an event-based observability product, trying to define all the other words that go around o11y at the same time.
In our world of distributed systems, state changes to your infrastructure often take some time to propagate. With a few exceptions (for example, feature flags), single point in time changes are rare. Deploys, outages, database migrations, failovers, stress tests; none of these things are instantaneous – all have some duration during which the system is changing.
