Operations | Monitoring | ITSM | DevOps | Cloud

November 2018

Diving into Data with Honeycomb: "Codename: Drilldown" is in Beta!

This blog miniseries talks about how to think about doing data analysis the Honeycomb way. In this episode, we announce an exciting new feature, currently in beta. Honestly, we’re so excited to get this out the door, we haven’t settled on a final name so for now, we’re going with “Codename: Drilldown.”

Honeycomb and Rookout: An Integration That Finds the Dots to Connect

You probably know that Honeycomb is the most flexible observability tool around. Its powerful high-cardinality search makes working with real raw data quick and easy. But as you may have learned through hard experience, fetching those dots can still be quite a challenge.

Heatmaps Make Ops Better

In this blog miniseries, I’d like to talk about how to think about doing data analysis “the Honeycomb way.” Welcome to part 1, where I cover what a heatmap is—and how using them can really level up your ability to understand what’s going on with distributed software. Heatmaps are a vital tool for software owners: if you’re going to look at a lot of data, then you need to be able to summarize it without losing detail.

Level Up With Derived Columns: Wibbly-Wobbly Timey-Wimey Manipulation

When we released derived columns last year, we already knew they were a powerful way to manipulate and explore data in Honeycomb, but we didn’t realize just how many different ways folks could use them. We use them all the time to improve our perspective when looking at data as we use Honeycomb internally, so we decided to share. So, in this series, Honeycombers share their favorite derived column use cases and explain how to achieve them.

Speeding Things Up So Your Queries Can Bee Faster

Honeycomb strives to be a fast, efficient tool; our storage back-end satisfies the median customer query in 250ms (and the P90 in 1.3 seconds). Still, every system has its limits, and customers with large datasets know that querying over a long time range, grouping by high-cardinality columns, building complex derived columns, and throwing a quantile or heat map into the mix can lead to some pretty slow queries. If this sounds familiar: good news!