In my last blog post, I explained why we decided to destroy one third of our infrastructure in production just to see what would happen. This is part two, where I go over the big day. How did our chaos engineering experiment go? Find out below!

According to a recent study, failing tests alone cost the enterprise software market an astonishing $61 billion annually. This figure mirrors the vast number of resources devoted to rectifying software failures, translating into about 620 million developer hours lost each year. On average, engineers spend 13 hours to resolve a single software failure, a statistic that paints a stark picture of the current state of debugging efficiency.

Shubham Srivastava from our team had the pleasure of meeting Andreas Grabner at KubeCon + CloudNativeCon Europe earlier this year. Andreas wears many hats in his daily work, primarily serving as a DevOps Activist at Dynatrace, where he has dedicated over 16 years to shape the Observability solutions we see today. He is also a Developer Advocate at Keptn – helping teams automate and orchestrate their deployments end-to-end and plays an active role as an Ambassador in the CNCF community.

“O11y”, also known as Observability, is changing how we handle system performance. This guide will walk you through the essentials of O11y and how to implement it effectively. Let's dive in.

Software changes so rapidly that developing on the cutting edge of it cannot fall to a single person. When it comes to asynchronously disseminating information about projects, code comments, PR conversations, Slack, RFCs, and other investigatory documents do a wonderful job, but no amount of async communication replaces the magic of two brains bouncing ideas off of each other.

In today's fast-paced digital economy, businesses are constantly seeking innovative solutions to streamline their operations, enhance agility, and drive growth. As enterprise IT infrastructure environments get more distributed and complicated to meet evolving demands, the need for robust IT monitoring, management and automation becomes even more important.

We recently took a daring step to test and improve the reliability of the Honeycomb service: we abruptly destroyed one third of the infrastructure in our production environment using AWS’s Fault Injection Service. You might be wondering why the heck we did something so drastic. In this post, we’ll go over why we did it and how we made sure that it wouldn’t impact our service.

With the advent of byte code instrumentation (BCI) in 2008, application performance management took a giant leap in what is known as "inside-out monitoring," that is, monitoring from inside the application. Before that, application monitoring was largely limited to tracking CPU, memory, disk, and process availability. BCI offered new opportunities in terms of how applications could be monitored and what could be monitored from an application performance perspective.

Traditional monitoring has become insufficient for managing complex systems. Modern infrastructures consist of numerous interconnected services, and simply monitoring individual metrics and logs fails to provide a comprehensive view. This is where observability becomes crucial.