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By Rox Williams
ShipHero needed a robust, cost efficient observability platform to support DevOps, customer support, and more. Committed to timely service, ShipHero recognizes that the seamless performance of its software is paramount to customer satisfaction. To maintain this high standard, the development team needs the right data at their fingertips to quickly find and solve problems as they occur.
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By Jamie Danielson
Now that we’ve had time to decompress from KubeCon, we wanted to do a writeup about our collective experience. Six of us spoke at the conference and Charity participated in a panel, so we included short talk recaps.
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By Purvi Kanal
So you’ve taken a look at the core web vitals for your site and… it’s not looking good. You’re overwhelmed, and you don’t know what change to make because everything seems like too big of a project to make a real difference. There are so many measurements to keep track of and the standards cited seem even scarier. This is extremely normal. Web performance standards can feel impossible to meet for a lot of us.
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By Jamie Danielson
Observability is important to understand what’s happening in production. But carving out the time to add instrumentation to a codebase is daunting, and often treated as a separate task to writing features. This means that we end up instrumenting for observability long after a feature has shipped, usually when there’s a problem with it and we’ve lost all context. What if we instead treated observability similarly to how we treat tests?
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By George Miranda
Ever since we launched Query Assistant last June, we’ve learned a lot about working with—and improving—Large Language Models (LLMs) in production with Honeycomb. Today, we’re sharing those techniques so that you can use them to achieve better outputs from your own LLM applications. The techniques in this blog are a new Honeycomb use case. You can use them today. For free. With Honeycomb.
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By Mike Terhar
A lot of reasoning in content is predicated on the audience being in a modern, psychologically safe, agile sort of environment. It’s aspirational, so folks who aren’t in those environments may feel like the path there includes doing “the new thing” or using “the new tool.” If you write software and your employer hasn’t caught up to all the newest, best ways to work, I hope this pragmatic post helps you sleep better at night.
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By Emil Protalinski
Software systems are increasingly complex. Applications can no longer simply be understood by examining their source code or relying on traditional monitoring methods. The interplay of distributed architectures, microservices, cloud-native environments, and massive data flows requires an increasingly critical approach: observability.
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By Fred Hebert
As someone living the Honeycomb ops life for a while, SLOs have been the bread and butter of our most critical and useful alerting. However, they had severe, long-standing limitations. In this post, I will describe these limitations, and how our brand new feature, budget rate alerts, addresses them. We usually don’t have SREs writing product announcements, but I’m so excited about this one that I said, “Screw it, I’m doing it!”
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By Austin Parker
Stop me if you’ve heard this one before: you just pushed and deployed your latest change to production, and it’s rolling out to your Kubernetes cluster. You sip your coffee as you wrap up some documentation when a ping in the ops channel catches your eye—a sales engineer is complaining that the demo environment is slow. Probably nothing to worry about, not like your changes had anything to do with that… but, minutes later, more alerts start to fire off.
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By Mike Terhar
In telemetry jargon, a pipeline is a directed acyclic graph (DAG) of nodes that carry emitted signals from an application to a backend. In an OpenTelemetry Collector, a pipeline is a set of receivers that collect signals, runs them through processors, and then emits them through configured exporters. This blog post hopes to simplify both types of pipelines by using an OpenTelemetry extension called the Headers Setter.
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By Honeycomb
Honeycomb Customer Success Manager Josh Levin explains how to troubleshoot production incidents using Honeycomb's telemetry data: metrics, traces, and logs. While these data forms have separate interfaces, you can investigate seamlessly within Honeycomb. Josh highlights the key role of the "retriever" service in data ingestion and querying and demonstrates cross-validating tracing data with metrics to spot anomalies in pod deployments and resource usage, presented in a separate dataset. He also uses effective log filtering and searching for keywords like "update status.".
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By Honeycomb
Buildevents is a small binary used to help instrument builds to generate trace telemetry. It populates the trace with metadata from the GitHub Actions environment so you have details about what occurred throughout the entire build. In this tutorial, learn how to instrument with Buildevents and GitHub actions.
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By Honeycomb
Buildevents is a small binary used to help instrument builds to generate trace telemetry. It populates the trace with metadata from the GitHub Actions environment so you have details about what occurred throughout the entire build. In this tutorial, learn how to instrument with Buildevents and GitHub actions.
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By Honeycomb
Nathan Lincoln, an SRE at Honeycomb, walks through the basics of feature flag best practices (using LaunchDarkly) to help you maintain a stable system. Feature flags are useful for reducing outages and downtime in our systems by allowing traffic segmentation, but they can create chaos without proper maintenance.
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By Honeycomb
Tech debt. Vendor redundancy. System fragmentation. Startups and cloud–born companies are looking at vendors for cost-cutting opportunities. But how do you balance vendor costs and value when those resources and tools bring efficiencies as high as the monthly bills? In this session, Charity Majors and Gergely Orosz share advice on managing spend in a vendor-dependent world.
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By Honeycomb
Hear from Odyssey Interactive to learn how they implemented Honeycomb to accelerate development time and resolve issues quickly.
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By Honeycomb
Honeycomb's trace-aware proxy, Refinery, allows you to sample your data to reduce costs while giving you the ability to ensure the lowest margin of error on your most important data.
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By Honeycomb
Stepping through a trace is an invaluable debugging workflow, providing a way to follow requests from service to service even as the applications we manage become more complex and distributed. That same complexity can make getting started with distributed tracing feel overwhelming, but it’s important to remember that instrumenting your code is an additive process—you don’t need to boil the ocean. A trace through a thousand services starts with a single ID.
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By Honeycomb
Chris Bertinato, Software Architect at NS1, and Nate Daly, Head of Architecture at NS1 along with Jessica Kerr, Honeycomb Developer Advocate, and Account Executive Scott Phillips discuss how NS1 used distributed tracing to scale their organization and accelerate their migration from a monolith to microservices.
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By Honeycomb
Honeycomb is an event-based observability tool, but you can-and should-use metrics alongside your events. Fortunately, Honeycomb can analyze both types of data at the same time. When maturing from metrics-based application monitoring to an observability-based development practice, there are considerations that can make the transformation easier for you and your team.
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By Honeycomb
Evaluating observability tools can be a daunting task when you're unfamiliar with key considerations and possibilities. This guide steps through various capabilities for observability tooling and why they matter.
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By Honeycomb
This document discusses the history, concept, goals, and approaches to achieving observability in today's software industry, with an eye to the future benefits and potential evolution of the software development practice as a whole.
- December 2023 (3)
- November 2023 (8)
- October 2023 (7)
- September 2023 (9)
- August 2023 (8)
- July 2023 (7)
- June 2023 (14)
- May 2023 (16)
- April 2023 (12)
- March 2023 (22)
- February 2023 (7)
- January 2023 (8)
- December 2022 (3)
- November 2022 (8)
- October 2022 (16)
- September 2022 (10)
- August 2022 (11)
- July 2022 (11)
- June 2022 (14)
- May 2022 (6)
- April 2022 (14)
- March 2022 (8)
- February 2022 (9)
- January 2022 (9)
- December 2021 (11)
- November 2021 (7)
- October 2021 (9)
- September 2021 (5)
- August 2021 (4)
- July 2021 (6)
- June 2021 (11)
- May 2021 (2)
- April 2021 (4)
- March 2021 (5)
- February 2021 (9)
- January 2021 (6)
- December 2020 (9)
- November 2020 (7)
- October 2020 (18)
- September 2020 (11)
- August 2020 (4)
- July 2020 (6)
- June 2020 (4)
- May 2020 (6)
- April 2020 (7)
- March 2020 (5)
- February 2020 (4)
- January 2020 (5)
- December 2019 (3)
- November 2019 (3)
- October 2019 (2)
- September 2019 (7)
- August 2019 (6)
- July 2019 (5)
- June 2019 (6)
- May 2019 (6)
- April 2019 (4)
- March 2019 (3)
- February 2019 (3)
- January 2019 (4)
- December 2018 (9)
- November 2018 (8)
- October 2018 (2)
- September 2018 (5)
- August 2018 (11)
- July 2018 (7)
- June 2018 (7)
- May 2018 (7)
- April 2018 (3)
Honeycomb is a tool for introspecting and interrogating your production systems. We can gather data from any source—from your clients (mobile, IoT, browsers), vendored software, or your own code. Single-node debugging tools miss crucial details in a world where infrastructure is dynamic and ephemeral. Honeycomb is a new type of tool, designed and evolved to meet the real needs of platforms, microservices, serverless apps, and complex systems.
Honeycomb provides full stack observability—designed for high cardinality data and collaborative problem solving, enabling engineers to deeply understand and debug production software together. Founded on the experience of debugging problems at the scale of millions of apps serving tens of millions of users, we empower every engineer to instrument and query the behavior of their system.