As an application developer, have you ever had to troubleshoot an issue that only happens in production? Bugs can occur when your application gets released into the wild, and they can be extremely difficult to debug when you cannot reproduce without production data. In this blog, I am going to show you how to safely send your production data to development applications deployed using a service mesh to help you better debug and build production proof releases.

Welcome to part one of this two-part series on debugging in Elixir and Erlang. In this post, we'll use several different tools and techniques to debug Elixir code. First, we'll get to know the demo project I created to showcase certain tools: dist_messenger. It's an Elixir project of a distributed messaging system that can be used directly on IEx. It allows users in different nodes to send and receive messages to each other.

We always want to use the best of the best when it comes to Kubernetes tooling. We keep up with the latest projects, download them when ready to try them out, and see where they could fit in our development work here at Rookout. As part of our migration to Kubernetes native tools, our most important goal is to make it seamless and smooth so our developers will stay agile, fast, and happy. For some of that migration, we’ve faced some challenges, particularly with pipelines.

Every developer has experienced the pain of debugging. We know that you know what we’re talking about. Who hasn’t been lost jumping between different windows troubleshooting their code, log-jammed trying to track bugs in a pile of logs, or been caught screaming at a rubber duck that’s blankly staring back at them? But it shouldn’t be that way.

It doesn’t matter if you’re just starting to learn code or if you’ve been doing it for a few years – sometimes you feel like you have no idea what you’re doing. This feeling can be made even worse by the wealth of information out there, making the right decision can get stressful. Life is full of those tough choices: Coke vs. Pepsi; Yankees vs. Red Sox; Python vs. Flask. You know, regular questions about soda, baseball, and Python frameworks that almost everyone faces.