Honeycomb

San Francisco, CA, USA
2016
  |  By Terra Field
Earlier this year, we upgraded from Confluent Platform 7.0.10 to 7.6.0. While the upgrade went smoothly, there was one thing that was different from previous upgrades: due to changes in the metadata format for Confluent’s Tiered Storage feature, all of our tiered storage metadata files had to be converted to a newer format.
  |  By Nick Travaglini
As our Field CTO Liz Fong-Jones says, production excellence is important for cloud-native software organizations because it ensures a safe, reliable, and sustainable system for an organization’s customers and employees. A CoPE helps organizations cultivate the practices and tools necessary to achieve that consistently. In part one of our CoPE series, we analogized the CoPE with safety departments.
  |  By Hazel Edmands
Our storage engine, affectionately known as Retriever, has served us faithfully since the earliest days of Honeycomb. It’s a tool that writes data to disk and reads it back in a way that’s optimized for the time series-based queries our UI and API makes. Its architecture has remained mostly stable through some major shifts in the surrounding system it supports, notably including our 2021 implementation of a new data model for environments and services.
  |  By Rox Williams
Amperity required an observability partner to facilitate their transition into the modern engineering era as their previous tooling struggled to support their growth strategy. When customer data is scattered everywhere, how do you put the pieces together to get an accurate customer 360° view? That’s the power of Amperity’s customer data platform (CDP), and the company has been driving customer data innovation for nearly a decade.
  |  By Charity Majors
In my February 2024 piece The Cost Crisis in Observability Tooling, I explained why the cost of tools built atop the three pillars of metrics, logs, and traces—observability 1.0 tooling—is not only soaring at a rate many times higher than your traffic increases, but has also become radically disconnected from the value those tools can deliver. Too often, as costs go up, the value you derive from these tools declines.
  |  By Rox Williams
Over the past five years, software and systems have become increasingly complex and challenging for teams to understand. A challenging macroeconomic environment, the rise of generative AI, and further advancements in cloud computing compound the problems faced by many organizations. Simply understanding what’s broken is difficult enough, but trying to do so while balancing the need to constantly innovate and ship makes the problem worse.
  |  By Josephine Yuan
We recently introduced relational fields, a new feature that allows you to query spans based on their relationship to each other within a trace. You can now query for spans where its root span, direct parent span, or any other single span in the trace has certain attributes. We currently support the following three prefixes: root. - Identifies the root span within a trace. To find a match, any additional root. filters in your query will search through fields only in the specified root. span.
  |  By Rox Williams
Fender faced challenges with log analysis, finding it slow and complex to navigate, leading to inefficient troubleshooting and a need for a more user-friendly and modern observability solution. Synonymous with all things rock n’ roll, Fender is the world’s leading guitar manufacturer. To enhance the customer experience, Fender launched their digital apps in 2016 (Fender Tune and Fender Tone) and 2017 (Fender Play) to empower customers in starting and advancing their guitar playing skills.
  |  By Winston Hearn
Today, we're announcing the early access program of Honeycomb for Frontend Observability. Honeycomb for Frontend Observability gives teams the ability to quickly identify opportunities for optimization within their web app. This starts with better OpenTelemetry instrumentation, available as an NPM package, that lets you instrument and collect attribution data on Core Web Vitals in under an hour.
  |  By Aiden Senner
The 1981 book Simulacra and Simulation by Jean Baudrillard is widely read and cited within academic circles but also permeates popular culture, influencing films, literature, and art. His theories notably influenced the Wachowski siblings' The Matrix series, bringing some of his ideas into mainstream awareness.
  |  By Honeycomb
What is the biggest value of #observability as practiced on the #backend that you are excited to see taken up as more #frontend #developers start practicing observability on their own? Featuring: Winston Hearn, Frontend Observability Expert and Hazel Weakly, Web Developer and #SRE.
  |  By Honeycomb
While baggage isn’t required for distributed tracing, it is required for carrying metadata between services. How will the observability community address that and make it easier over time? Featuring: Winston Hearn, Frontend Observability Expert and Hazel Weakly, Web Developer and SRE.
  |  By Honeycomb
Featuring: Winston Hearn, Frontend Observability Expert and Hazel Weakly, Web Developer and SRE.
  |  By Honeycomb
What kind of questions can you ask about your #frontend with #distributedtracing? Featuring: Winston Hearn, Frontend #Observability Expert and Hazel Weakly, Web Developer and SRE.
  |  By Honeycomb
How does the usefulness of auto-instrumentation differ for the #frontend versus the #backend?
  |  By Honeycomb
As a new company poised to transform the financial services industry with its modern money movement platform, Moov wanted an equally modern observability platform as part of the company’s operational tech stack.
  |  By Honeycomb
In this three minute clip from our recent webinar with DORA's Nathen Harvey, Charity Majors explains observability 1.0 versus observability 2.0.
  |  By Honeycomb
In this three minute clip from our recent webinar with DORA's Nathen Harvey, Charity Majors explains observability 1.0 versus observability 2.0.
  |  By Honeycomb
If you wrote a query but realized you were in the wrong environment, here's how you can avoid having to rewrite your query by copying the JSON. Thanks Jessitron for making this helpful video!
  |  By Honeycomb
Imagine a universe in which a massively multiplayer online role-playing game (MMORPG) sets Guinness World Records for the size of its online space battles—and that game is built on 20-year-old code. Well, imagine no more. Welcome to the world of EVE Online, where hundreds of thousands of players interact across 7,800+ star systems and participate in more than one million daily market transactions. As you might guess, updating and maintaining this codebase without interrupting game play could pose quite a challenge.
  |  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.
  |  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.
  |  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.

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.