But it’s better than nothing… Most of the industry is racing to adopt better observability practices, and they’re discovering lots of power in being able to see and measure what their systems are doing. High data availability is better than none, so for the time being, what we get is often impressive. There’s a qualitative difference between observability and data availability, and this post aims to highlight it and orient how we structure our telemetry.

Today, we released our second annual Observability Maturity Community Research Findings report. This year-over-year report identifies trends occurring in the observability community that we use to further develop our Observability Maturity Model. Our goal in running this annual report is to understand community perceptions and awareness of observability, how engineering teams are approaching observability, and mapping an observability maturity model that reflects current research findings.

Today at o11ycon+hnycon—right now, actually, if you’re reading this blog when it was posted—we’re announcing several new Honeycomb features during the keynote. Our industry and community have come a long way since we burst onto the scene, and I’m delighted to give you another version of Honeycomb that continues to demonstrate what’s possible with observability. And it includes metrics.

With the Honeycomb extension for AWS Lambda, you no longer need to make your Lambda functions Honeycomb-aware. Today, AWS announced the general availability of AWS Lambda Extensions, which make it easy for us to send logs from your Lambda functions directly to Honeycomb. In October, we announced Honeycomb’s extension for AWS Lambda as part of a preview launch. Today, we’re pleased to announce everyone can now use this extension to easily debug their AWS Lambda functions with Honeycomb.

In case you missed it, this webinar includes code walkthroughs that help you to add observability to your pipelines (using a free Honeycomb account!) so that you and your team can speed up your deployments to prod. This is also a risk-free way to get started with observability if your team isn’t quite yet ready to change your production apps.

During my office hours, I frequently get asked for practical tips on getting started with observability. Often it’s from folks on teams who are already practicing continuous delivery (or trying to get there) and are interested in more advanced practices like progressive delivery. They know observability can help—but as individual contributors—they don’t sign the checks, so they feel powerless to help get their team started with observability.

Starting today, Honeycomb’s Management API is generally available to all Honeycomb users. The Honeycomb Management API is a set of endpoints that lets you programmatically set up, configure, and delete queries, datasets, derived columns, and more. With this release, you can now manage Honeycomb with configuration as code either directly via API or with third-party tools, like Terraform, using the community-contributed Honeycomb provider.

Over the last few months, Honeycomb’s platform team migrated to a new iteration of our ingest pipeline for customer events. Our migration to this newer architecture did not go too smoothly, as can be attested by our status page since February. There were also many near-incidents where we got paged and reacted quickly enough to avoid major issues. We’ve decided to write a full overview of all the challenges we had encountered, which you can can download.

A year ago, we wrote about our experiences as early adopters of Graviton2, and how we were able to see 30% price-performance improvements on one dogfood workload from switching to the arm64 architecture. In those initial experiments, we validated running 20% fewer shepherd ingest workers, using the m6g instance type, which cost 10% less per instance compared to c5 instances.

The current landscape of what our customers are dealing with in monitoring and observability can be a bit of a mess. For one thing, there are varying expectations and implementations when it comes to observability data. For another, most customers have to lean on a hodgepodge of tools that might blend open source and proprietary, require extensive onboarding as team members have to learn which tools are used for what, and have a steep learning curve in general.