Experiments in Daily Work
TL;DR: Sometimes I get hung up in the scientific definition of "experiment." In daily work, take inspiration from it. Mostly, remember to look at the results.
TL;DR: Sometimes I get hung up in the scientific definition of "experiment." In daily work, take inspiration from it. Mostly, remember to look at the results.
Whether you’re a new Honeycomb user or a seasoned expert looking to uncover fresh insights, chances are you’ve sent tremendous amounts of data into Honeycomb already. The question is, now what? We have the answer: Board templates. Teams can now create Boards based on pre-built templates that generate visualizations with a single click.
This guest post is written by Ian Duncan, Staff Engineer - Stability Team at Mercury. To view the original post, go to Ian's website. At work, we use OpenTelemetry extensively to trace execution of our Haskell codebase. We struggled for several months with a mysterious tracing issue in our production environment wherein unrelated web requests were being linked together in the same trace, but we could never see the root trace span.
The OpenTelemetry Collector is a useful application to have in your stack. However, deploying it has always felt a little time consuming: working out how to host the config, building the deployments, etc. The good news is the OpenTelemetry team also produces Helm charts for the Collector, and I’ve started leveraging them. There are a few things to think about when using them though, so I thought I’d go through them here.
On July 25th, 2023, we experienced a total Honeycomb outage. It impacted all user-facing components from 1:40 p.m. UTC to 2:48 p.m. UTC, during which no data could be processed or accessed. This outage is the most severe we’ve had since we had paying customers. In this review, we will cover the incident itself, and then we’ll zoom back out for an analysis of multiple contributing elements, our response, and the aftermath.
What do mall food courts and Honeycomb have in common? We both love sampling! Not only do we recommend it to many of our customers, we do it ourselves. But once Refinery (our tail-based sampling proxy) is set up, what comes next? Since sampling is inherently lossy, it’s good to be sure the organization’s most important measurements aren’t negatively affected.
Our friends at Tracetest recently released an integration with Honeycomb that allows you to build end-to-end and integration tests, powered by your existing distributed traces. You only need to point Tracetest to your existing trace data source—in this case, Honeycomb. This guest post from Adnan Rahić walks you through how the integration works.
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-restore, and change fail rate. Observability can kickstart a virtuous cycle that improves all the DORA metrics.
The needs of observability workloads can sometimes be orthogonal to the needs of compliance workloads. Honeycomb is designed for software developers to quickly fix problems in production, where reducing 100% data completeness to 99.99% is acceptable to receive immediate answers. Compliance and audit workloads require 100% data completeness over much longer (or "infinite") time spans, and are content to give up query performance in return.