Honeycomb Blog
Announcing Honeycomb’s Java SDK
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. Installation The SDK is available from Maven Central Repository. To get started, add this to your Maven build’s pom.xml: <dependency> <groupId>io.honeycomb.libhoney</groupId>…
There And Back Again: A Honeycomb Tracing Story
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. To avoid this annoying and…
Level Up With Derived Columns: Understanding Screen Size (With Basic Arithmetic)
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…
Instrument Your Python App Automatically With The Honeycomb Beeline for Python
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! The Python Beeline automatically instruments HTTP requests and DB queries and…
Level Up With Derived Columns: Basic Comparisons
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…
How Are Structured Logs Different From Events?
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. One of the questions I’ve seen come up several times in different forums recently is around the…
Use New Range Markers to Show The Duration of a State Change
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…
o11ycon: A Conference For The Observability Community
Hey friends, We recently announced o11ycon, and the response has been terrific! Especially since we haven’t really even told you all what we’re planning. 🙂 First things first: o11y means observability. (Yes, abbreviations are annoying, but YOU try typing ‘observability’ twenty times a day with character limits. And o11y just sounds so cute!) o11ycon will…
How Honeycomb Uses Honeycomb, Part 9: Tracing The Query Path
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 a great job of telling the story of problems we’ve solved with Honeycomb. Last week we announced the general availability of…
Instrument Your Ruby App In Minutes with the Honeycomb Beeline for Ruby
You’ve always been able to get observability for your Ruby apps by instrumenting them with our SDK, affectionately known as libhoney, but we know instrumenting code you’ve already written is nobody’s favourite job. If only there were some way to automate the repetitive parts, so you could get instant insight into what your app is doing…