Operations | Monitoring | ITSM | DevOps | Cloud

March 2023

Twelve-Factor Apps and Modern Observability

The Twelve-Factor App methodology is a go-to guide for people building microservices. In its time, it presented a step change in how we think about building applications that were built to scale, and be agnostic of their hosting. As applications and hosting have evolved, some of these factors also need to. Specifically, factor 11: Logs (which I’d also argue should be a lot higher up in the ordering).

Trace at Your Own Pace: Three Easy Ways to Get Started with Distributed Tracing

Stepping through a trace is an invaluable debugging workflow, providing a way to follow requests from service to service even as the applications we manage become more complex and distributed. That same complexity can make getting started with distributed tracing feel overwhelming, but it’s important to remember that instrumenting your code is an additive process—you don’t need to boil the ocean. A trace through a thousand services starts with a single ID.

Learn How NS1 Uses Distributed Tracing to Release Code More Quickly and Reliably

Chris Bertinato, Software Architect at NS1, and Nate Daly, Head of Architecture at NS1 along with Jessica Kerr, Honeycomb Developer Advocate, and Account Executive Scott Phillips discuss how NS1 used distributed tracing to scale their organization and accelerate their migration from a monolith to microservices.

Discover Unknown Service Interaction Patterns With Istio & Honeycomb

Istio service meshes enable organizations to secure, connect, and monitor microservices to modernize their enterprise apps more swiftly and securely. With the addition of distributed tracing and powerful observability tooling, platform operators can gain immediate actionable insights about their applications.

Intercom: Building a More Resilient Ecosystem Through Observability

Learn how Intercom implemented Honeycomb’s distributed traces to learn about production. Kesha Mykhailov, Product Engineer at Intercom joins Honeycomb Developer Advocate Jessica Kerr, and Account Executive Michael Wilde to discuss how Intercom uses distributed traces to streamline their observability workflows, allowing their product engineers to learn about and from their production to increase Intercom’s resilience. Topics include.

Surface and Confirm Buggy Patterns in Your Logs Without Slow Search

Debugging with logs in distributed systems can be a pain. It’s tough to search raw data looking for a pattern, relating potential causes with other logs, and checking trace and metrics data for more confirmation. Is finding one pattern enough? What if there are other problems? Who knows how many colliding factors are relevant? At Honeycomb, we’re flipping the script on the log search problem. Hear our resident experts, (former Splunk Ninja) Michael Wilde and Andy Dufour, discuss how Honeycomb customers have technically evolved their log analysis process to achieve fast pattern detection, skipping the search grep/search loop entirely.

See How Coveo Engineers Reduced User Latency

Many teams are wasting far too much time and energy searching through massive amounts of log data trying to find answers to user latency issues. Metrics data doesn’t help either as it only tells you that there is a problem, not where to fix it. This is why Coveo turned to observability. Through implementing observability with Honeycomb, Coveo was able to reduce their user latency by 50 percent.

Join Jeli and Honeycomb for an Incident Response and Analysis Discussion

Solutions Engineers Vanessa Huerta Granda and Emily Ruppe from Jeli, along with Honeycomb’s Field CTO Liz Fong-Jones and SRE Fred Hebert discuss some of our more interesting recent incidents and how we use Honeycomb and Jeli together for incident response.

Learn How SumUp Implemented SLOs to Mitigate User Outages and Reduce Customer Churn

Blake Irvin and Matouš Dzivjak from SumUp’s Software Engineering team, Honeycomb Solution Architect Michael Sickles and Account Executive Nathan Leary, discuss how SumUp incorporated observability, specifically, SLOs, to identify and resolve issues before they grew into customer-noticeable problems.

Ask Miss O11y: Is There a Beginner's Guide On How to Add Observability to Your Applications?

I want to make my microservices more observable. Currently, I only have logs. I’ll add metrics soon, but I’m not really sure if there is a set path you follow. Is there a beginner's guide to observability of some sort, or best practice, like you have to have x kinds of metrics? I just want to know what all possibilities are out there. I am very new to this space.

How Do We Cultivate the End User Community Within Cloud-Native Projects?

The open source community talks a lot about the problem of aligning incentives. If you’re not familiar with the discourse, most of this conversation so far has centered around the most classic model of open source: the solo unpaid developer who maintains a tiny but essential library that’s holding up half the internet. For example, Denis Pushkarev, the solo maintainer of popular JavaScript library core-js, announced that he can’t continue if not better compensated.

Platform Engineering Is the Future of Ops

Ops and DevOps roles as we know them are on their way to becoming extinct—the future is platform engineering. While DevOps engineers typically focus on the application layer, platform engineers focus on the underlying infrastructure layer. Without a solid and reliable platform, it can be challenging to deploy and maintain software applications effectively. This can result in downtime, poor performance, and security vulnerabilities. Platform engineering enables software applications and services to run effectively and efficiently and has a direct impact on the user experience and the success of the entire organization.

How We Define SRE Work, as a Team

Last year, I wrote How We Define SRE Work. This article described how I came up with the charter for the SRE team, which we bootstrapped right around then. It’s been a while. The SRE team is now four engineers and a manager. We are involved in all sorts of things across the organization, across all sorts of spheres. We are embedded in teams and we handle training, vendor management, capacity planning, cluster updates, tooling, and so on.

Deploys Are the WRONG Way to Change User Experience

I'm no stranger to ranting about deploys. But there's one thing I haven't sufficiently ranted about yet, which is this: Deploying software is a terrible, horrible, no good, very bad way to go about the process of changing user-facing code. It sucks even if you have excellent, fast, fully automated deploys (which most of you do not). Relying on deploys to change user experience is a problem because it fundamentally confuses and scrambles up two very different actions: Deploys and releases.

How Coveo Reduced User Latency and Mean Time to Resolution with Honeycomb Observability

When you’re just getting started with observability, a proof of concept (POC) can be exactly what you need to see the positive impact of this shift right away. Coveo, an intelligent search platform that uses AI to personalize customer interactions, used a successful POC to jumpstart its Honeycomb observability journey—which has grown to include 10,000+ machine learning models in production at any one time. Wondering how Coveo got there? So were we.

Caring for Complex Systems: We Can Do This

When we work at it, professionals are pretty good at analysis. We can break down a simple system, look at its parts and their relations, and master it. Given enough time and teammates, we can analyze a very complicated system and fix it when it breaks. But complex systems don’t yield to analysis. We have to add another skill: sense-making. Complex systems have parts that learn and change, with relations that vary with state and history. They respond to and influence their environment.

Understanding Distributed Tracing with a Message Bus

So you're used to debugging systems using a distributed trace, but your system is about to introduce a message queue—and that will work the same… right? Unfortunately, in a lot of implementations, this isn't the case. In this post, we'll talk about trace propagation (manual and OpenTelemetry), W3C tracing, and also where a trace might start and finish.

How 3 Companies Implemented Distributed Tracing for Better Insight into Their Systems

Distributed tracing enables you to monitor and observe requests as they flow through your distributed systems to understand whether these requests are behaving properly. You can compare tiny differences between multiple traces coming through your microservices-based applications every day to pinpoint areas that are affecting performance. As a result, debugging and troubleshooting are simpler and faster.