RUM can leave questions unanswered. Honeycomb for Frontend Observability (now in EA) doesn’t.

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Investigating Mysterious Kafka Broker I/O When Using Confluent Tiered Storage
Investigating Mysterious Kafka Broker I/O When Using Confluent Tiered Storage

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.

Independent, Involved, Informed, and Informative: The Characteristics of a CoPE
Independent, Involved, Informed, and Informative: The Characteristics of a CoPE

In part one of our CoPE series, we analogized the CoPE with safety departments. David Woods says that those safety departments must be: independent, involved, informed, informative. In this post, we’ll elaborate on what each of those characteristics means, why the CoPE should also match those qualifications, and how to achieve that status.

Virtualizing Our Storage Engine
Virtualizing Our Storage Engine

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. As usage of this feature has grown, however, we’ve noticed Retriever creaking in novel ways, pushing us to reconsider a core architectural choice.