Tom Granot and myself have had the privilege of Vlad Mihalcea’s online company for a while now. As a result we decided to do a workshop together talking about a lot of the things we learned in the process. This workshop would be pretty informal ad-hoc, just a bunch of guys chatting and showing off what we can do with tooling.
For the last five years, Logz.io has tracked and measured the pulse of DevOps, as well as adoption of key trends and technology, through our DevOps Pulse survey and report. One of the obvious focus areas for us, as a company whose products are based on industry-leading open source, is the increased rise of incredibly useful open-source observability solutions, in general.
We are all moving towards a digital workplace - or a hybrid work scenario. Whatever be the case, you can expect end-users to call and complain about a poor WiFi experience. That's because network monitoring needs to be done from their standpoint, not from the enterprise end. And without the correct WiFi observability data, it's challenging to narrow down the root cause of the problem affecting remote employees. And those problems - poor WiFi performance leading to poor digital experience - can be pervasive and persistent.
I spent over a decade as a consultant working for dozens of companies in many fields and pursuits. The diversity of each code base is tremendous. This article will try to define general rules for modernizing legacy code that would hopefully apply to all. But it comes from the angle of a Java developer. When writing this, my primary focus is on updating an old Java 6 era style J2EE code to the more modern Spring Boot/Jakarta EE code.
As systems get ever more complicated with more layers in the application stack, it can become more difficult to keep track of what is happening at all of the different, discrete levels and this is where distributed tracing comes into play.
When do you delete instrumentation? You delete instrumentation when you delete code. Other than that, if you’re doing things right: almost never. One of the best things about honeycomb is that it completely transforms the incentives around preserving instrumentation. With metrics-based tools, the most valuable metrics are always custom metrics. You need to define a custom metric for literally any question you might ever want to ask about the app and its utilization or performance.
Any significant shift in an organization’s software engineering culture has the potential to feel tectonic, and observability (o11y for short)—or more specifically, Observability Driven Development—is no different. Leaning into observability, which calls for tool-enhanced investigation, hypothesis testing, and data richness can be cumbersome even for the most veteran of teams.
Getting a good grasp on your application, especially when it is distributed across multiple clouds, kubernetes clusters and serverless functions is not an easy fit.
Miss O11y is delighted to welcome our newest band member: Martin Thwaites! Martin has been a member of the Honeycomb user community practically since its inception. He is a UK-based consultant who specializes in helping teams scale up and tackle challenging business problems, and a long-time contributor to the Azure and.NET communities. We think he looks ✨amazing✨ in a tiara.
