Why we invested in Honeycomb’s Series A

Honeycomb is a new company from a rockstar team from Facebook’s engineering organization. Honeycomb brings to market a new SaaS product that sits at the intersection of two multi-billion dollar markets: Metrics Monitoring/APM (Datadog, AppDynamics, New Relic) and Log Management/Search (Splunk and Elastic). As opposed to APM’s rigid structure that requires custom, predefined views and Log Management’s unwieldy, inflexible data heap, Honeycomb gives DevOps people the amazing ability to ask questions about their data lightning fast and on the fly. The ability to flexibly, ad hoc slice and dice through application and IT data — across billions of users and many dimensional attributes — solves for a burgeoning category called “Observability.”

What is “Observability”? Why does it matter?

Observability is a superset of IT monitoring that helps companies answer why a system or application is not working. As context, modern applications and IT environments are substantially larger and more complex than years ago, which has created a need for new solutions that provide flexibility, visibility, and speed that existing monitoring tools lack. The best, most concise definition I’ve found comes from Charity, CEO and Cofounder of Honeycomb.

“Observability focuses on the development of the application, and the rich instrumentation you need, not to poll and monitor it for thresholds or defined health checks, but to ask any arbitrary question about how the software works.” — Charity Majors, CEO and Cofounder, Honeycomb

Read Charity’s full description here.

How Honeycomb solves for Observability in a new way

To help frame what each category (Metrics Monitoring/APM and Log Management) does and how Honeycomb is unique, below is the known vs unknown matrix (referring to Donald Rumsfeld’s “known and unknown” construct — and yes, I’m referring to our former Secretary of Defense).

• Known-knowns (Facts): Metrics Monitoring and APM systems solve this well because these are known, predefined attributes that are understood and intentionally being monitored. These solutions typically store data in rigid schemas and users must create a new dashboard/report for each view. They are not intended for ad hoc ‘questions’/queries based on how the data is stored and their databases typically fall over with lots of attributes.
• Known-unknowns (Questions): Log Management solutions excel here where all types of data (structured/semi-structured) is captured first and queried for search query later. Most Log Management systems are well-suited for string searches and their data stores are optimized for that, so high number of dimensions, multiple levels of filtering, or cardinality often makes them extremely slow or unusable.
• Unknown-unknowns (Exploration/Discovery): Honeycomb is particularly well suited for discovery, based on its architecture and data model. If any dimension is the root cause for an issue, one does not need to predefine a dashboard or report against that dimension — Honeycomb can surface this ad hoc and in real-time. Additionally, if any specific customer/group/ID (cardinality) is the source of a issue, Honeycomb can ‘zoom’ in *and* out against multiple filters to find the cause. This is immensely powerful for users, providing them with a unique solution with high performance, highly flexible, and unlimited-permutation capabilities for discovery (a la Tableau).

In short, navigating the unknown-unknowns is a new category that has the potential of transforming how DevOps teams work — removing the pain of building thousands of dashboards that only really get you to mere known-knowns. Now you can fully “interrogate” the system no matter how many needles are there.

Read more about the funding on Honeycomb’s blog.

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