The Most Important Developer Productivity Metric
At Honeycomb, we believe in ensuring an awesome developer experience for our own engineering teams; it's the only way that we can compete with companies...
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...
Achieving Great Dynamic Sampling with Refinery
Refinery, Honeycomb’s tail-based dynamic sampling proxy, often makes sampling feel like magic. This applies especially to dynamic sampling, because it ensures that interesting and unique...
Scaling Ingest With Ingest Telemetry
With the introduction of Environments & Services, we’ve seen a dramatic increase in the creation of new datasets. These new datasets are smaller than ones...
What Is Auto-Instrumentation?
In the past, we’ve written about what instrumentation is and the insights it provides. Instrumenting your code generates telemetry that shows you how your system...
Scaling Kafka at Honeycomb
When you send telemetry into Honeycomb, our infrastructure needs to buffer your data before processing it in our "retriever" columnar storage database. For the entirety...
How Time Series Databases Work—and Where They Don't
In my previous post, we explored why Honeycomb is implemented as a distributed column store. Just as interesting to consider, though, is why Honeycomb is...