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By Nick Travaglini
The CoPE is made to affect, meaning change, how things work. The disruption it produces is a feature, not a bug. That disruption pushes things away from a locally optimal, comfortable state that generates diminishing returns. It sets things on a course of exploration to find new terrains which may benefit it more—and for longer.
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By Lex Neva
In my last blog post, I explained why we decided to destroy one third of our infrastructure in production just to see what would happen. This is part two, where I go over the big day. How did our chaos engineering experiment go? Find out below!
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By Ruthie Irvin
Software changes so rapidly that developing on the cutting edge of it cannot fall to a single person. When it comes to asynchronously disseminating information about projects, code comments, PR conversations, Slack, RFCs, and other investigatory documents do a wonderful job, but no amount of async communication replaces the magic of two brains bouncing ideas off of each other.
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By Lex Neva
We recently took a daring step to test and improve the reliability of the Honeycomb service: we abruptly destroyed one third of the infrastructure in our production environment using AWS’s Fault Injection Service. You might be wondering why the heck we did something so drastic. In this post, we’ll go over why we did it and how we made sure that it wouldn’t impact our service.
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By Rox Williams
Transitioning from a monolithic system to a cloud-native microservices environment, Ritchie Bros. sought to modernize their observability infrastructure to support the transition and fuel future growth. Ritchie Bros. has been a pioneering force in the auctioneering market for nearly 70 years, charting remarkable growth through a strategic mix of organic expansion and acquisitions.
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By Nick Travaglini
Getting the right people working in the CoPE is crucial to success because these change agents must limber up the organization and promote the flexibility necessary to perform resilience. We’ll look for teammates who share enough in common to work well together, but who don’t necessarily perfectly overlap so that they can play off each other’s strengths.
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By Liz Fong-Jones
Two years ago, we shared our experiences with adopting AWS Graviton3 and our enthusiasm for the future of AWS Graviton and Arm. Once again, we're privileged to share our experiences as a launch customer of the Amazon EC2 R8g instances powered by AWS Graviton4, the newest generation of AWS Graviton processors. This blog elaborates our Graviton4 preview results including detailed performance data. We've since scaled up our Graviton4 tests with no visible impact to our customers.
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By Rox Williams
The Bennett Institute for Applied Data Science at the University of Oxford is pioneering the better use of data, evidence, and digital tools in healthcare, policy, and beyond. The institute employs an open-source approach with its OpenSAFELY analytics platform, enabling high-impact research that yields actionable insights, drives innovation, and enhances lives globally.
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By Tyler Wilson
Generative AI is having a bit of a moment—well, maybe more than just a bit. It’s an exciting time to be alive for a lot of people. But what if you see stories detailing a six month old AI firm with no revenue seeking a $2 billion valuation and feel something other than excitement in the pit of your stomach? Phillip Carter has an answer for you in his recent talk at Monitorama 2024. As he puts it, “you can keep being a hater, but you can also be super useful, too!”
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By Rox Williams
With a diverse range of applications, HappyCo sought to advance their system investigations with a modern observability solution while embarking on an application refactor project. Since its start in 2011, HappyCo has experienced rapid growth through both organic expansion and strategic acquisitions. As a result, the company has a diverse range of applications for customers to smile about.
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By Honeycomb
Martin investigates: what database queries are taking the longest? Then he digs into the one taking the most time, and asks: What user-initiated requests trigger this query? This kind of question helps developers focus our efforts where they count. And it's possible in Honeycomb with Relational Fields. This is #observability during development, using #OpenTelemetry #tracing and Honeycomb.
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By Honeycomb
In this quick tutorial video, Jessitron walks you through what to do if you create a #query in Honeycomb in the wrong environment. Chapters.
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By Honeycomb
#kubernetes helps teams of all sizes optimize their #microservices architecture by enabling seamless automated containerized app deployment, easy scalability, and efficient operations.
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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.
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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.
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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.
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By Honeycomb
How does the usefulness of auto-instrumentation differ for the #frontend versus the #backend?
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By Honeycomb
Featuring: Winston Hearn, Frontend Observability Expert and Hazel Weakly, Web Developer and SRE.
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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.
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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.
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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.
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- December 2018 (9)
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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.