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By Charity Majors
We’ve been talking about observability 2.0 a lot lately; what it means for telemetry and instrumentation, its practices and sociotechnical implications, and the dramatically different shape of its cost model. With all of these details swimming about, I’m afraid we’re already starting to lose sight of what matters.
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By Rox Williams
In the software space, we spend a lot of time defining the terminology that describes our roles, implementations, and ways of working. These terms help us share fundamental concepts that improve our software and let us better manage our software solutions. To optimize your software solutions and help you implement system observability, this blog post will share the key differences between logs vs traces.
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By Fred Hebert
About a year ago, Honeycomb kicked off an internal experiment to structure how we do incident response. We looked at the usual severity-based approach (usually using a SEV scale), but decided to adopt an approach based on types, aiming to better play the role of quick definitions for multiple departments put together. This post is a short report on our experience doing it.
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By Quinn Leong
Earlier this year, we introduced relational fields. Relational fields enable you to query spans based on their relationship to one other within a trace, rather than only in isolation. We’ve now expanded this feature and introduced four new prefixes: child., none., any2., and any3.. Previously, you could use root., parent., and any. to query on the root span of your target span’s trace, the parent span of your target span, and any other span in the same trace as your target span.
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By Brian Chang
Since its inception in 2004, Lansweeper has been at the forefront of helping businesses understand, manage, and protect their IT devices and networks through a powerful IT asset management platform. As the platform grew from an on-premises solution to a cloud-based SaaS offering, Lansweeper expanded its reach to a global, multi-region customer base.
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By Jessica Kerr
Observability means you know what’s happening in your software systems, because they tell you. They tell you with telemetry: data emitted just for the people developing and operating the software. You already have telemetry–every log is a data point about something that happened. Structured logs or trace spans are even better, containing many pieces of data correlated in the same record. But you want to start from what you have, then improve it as you improve the software.
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By Nick Travaglini
As discussed in the first article in this series, a Center of Production Excellence (CoPE) is a more or less formal, provisional subsystem within an organization. Its purpose is to act from within to change that organization so that it’s more capable of achieving production excellence. The series has, to date, focused mainly on how best to construct such a subsystem and what activities it should pursue.
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By Liz Fong-Jones
Let’s be real, we’ve never been huge fans of conventional unstructured logs at Honeycomb. From the very start, we’ve emitted from our own codestructured wide events and distributed traces with well-formed schemas. Fortunately (because it avoids reinventing the wheel) and unfortunately (because it doesn’t adhere to our standards for observability) for us, not all the software we run is written by us.
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By Mei Luo
At Honeycomb, we know how important it is for organizations to have a unified observability platform. This is why we’re launching Honeycomb Telemetry Pipeline and Honeycomb for Log Analytics: to enable engineering teams to send and analyze data—including logs—into a single, unified platform. For too long, teams have had to wrangle large volumes of logs, their context scattered across multiple teams and tools, leading to knowledge silos.
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By Elsie Phillips
Over the past six weeks, we introduced a series of impactful updates aimed at making your observability workflows faster, more unified, and more collaborative. Here’s a snapshot of what we worked on.
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By Honeycomb
Liz Fong-Jones walks you through how we debugged our Kubernetes Autoscaler with Honeycomb Log Analytics to achieve cost savings with Graviton4 instances. Having great observability is one way Honeycomb saves money.
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By Honeycomb
In this installment of Ask the Experts, Anijah asks Zach McCoy about distributed and what problems it solves.
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By Honeycomb
When an alert goes off because a Service Level Objective (SLO) is in danger of violation, it comes with a lot of context about what has been going wrong and for how long. Then Honeycomb gives you tools to explore the where & why. Here, Martin Thwaites walks through an example of diagnosing slower performance. What service is the problem, and under what circumstances?
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By Honeycomb
In this episode, Audrey Herndon, Technical Customer Success Manager at Honeycomb, asks #observability expert Dan Ravenstone at Top Hat, about the all-in-one solution.
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By Honeycomb
In this episode, Victoria Perera Roman, Sr. Technical Customer Success Manager at Honeycomb, asks #observability expert Chris Bertinato, Systems Architect at NS1, why some companies resist adopting Honeycomb.
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By Honeycomb
In this episode of Ask the Experts, Victoria Perera Roman, Sr. Technical Customer Success Manager at Honeycomb, asks Dominic Marino, Principal #software #engineer at Cargurus and #observability expert, why some companies resist adopting Honeycomb.
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By Honeycomb
There's a new way to flip through your data in Honeycomb, released this week! It's super for looking at structured logs. It's called: Explore Data. Get directly at the logs, spans, events, or metrics that power the fast analysis you can do with Honeycomb. See all the fields, the whole variety of values — now ordered by timestamp, with pagination. Modify your query and graphs right from the data table. It's all connected!
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By Honeycomb
See how Honeycomb gives you quick insight into web site performance, including Honeycomb gives you the metrics you need to see, and all the context you need to improve them.
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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
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.
- November 2024 (3)
- October 2024 (14)
- September 2024 (12)
- August 2024 (10)
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- June 2024 (9)
- May 2024 (10)
- April 2024 (8)
- March 2024 (10)
- February 2024 (11)
- January 2024 (8)
- December 2023 (6)
- 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.