So last time I mentioned, that another Kubernetes compatible service mesh – Conduit – has chosen another approach to solve the problem. Instead of enabling the mesh at machine level via e.g. http_proxy env variable, it connects k8s pods or deployments to it one by one. I really like such kinds of ideas that make 180° turn on solving the problem, so naturally I wanted to see how exactly they did that. Continue reading “Service mesh implemented via iptables”
I’ve been looking through the latest Technology Radar issue and here’s what I found in its new Techniques section: “TDD’ing containers”. Wow. Mentally, I’m not yet ready to connect TDD to containers, but I took a look at the tools used for that, and those are quite interesting.
The first one is serverspec, which allows running BDD-like tests against local or remote servers or containers. It looks pretty solid, supports multiple OSs and its only downside (for me) is that serverspec is written in Ruby and therefore doesn’t really fit in the stack I normally work with.
Most of the last week I’ve been experimenting with our .NET Windows project running on Linux in Kubernetes. It’s not as crazy as it sounds. We already migrated from .NET Framework to .NET Core, I fixed whatever was incompatible with Linux, tweaked here and there so it can run in k8s and it really does now. In theory.
In practice, there’re still occasional StackOverflow exceptions (zero segfaults, however) and most of troubleshooting experience I had on Windows is useless here on Linux. For instance, very quickly we noticed that memory consumption of our executable is higher than we’d expect. Physical memory varied between 300 MiB and 2 GiB and virtual memory was tens and tens of gigabytes. I know in production we could use much higher than that, but here, in container on Linux, is that OK? How do I even analyze that?
On Windows I’d took a process dump, feed it to Visual Studio or WinDBG and tried to google what’s to do next. Apparently, googling works for Linux as well, so after a few hours I managed learn several things about debugging on Linux and I’d like to share some of them today. Continue reading “Analyzing .NET Core memory on Linux with LLDB”
So far all examples I made for Docker in Swarm Mode or Kubernetes blog posts were built around some sort of a service: web server, message queue, message bus. After all, “service” is a main concept in Swarm Mode, and even the whole micro-service application thing has, well, a “service” in it. But what about one-off jobs: maintenance tasks, scheduled events, or anything else, that we need to run just sometimes, not as a service?
Much to my surprise, starting from the last week Kubernetes became the part of my job description. It’s no longer something just interesting to try, I actually have to understand it now. And as you probably could tell from my older k8s post, I’m not quite there. The post sort of builds a logical example (containerized web server) but something just doesn’t click.
I was trying to understand what’s missing, and it seems like the problem is in the tooling. You see, there’re two and a half ways to run something in Kubernetes. One is through ad-hoc commands, like kubectl run or kubectl expose. They are simple, but they also skip few important concepts happening in the background, so the whole picture stays unclear. Continue reading “Dissecting Kubernetes example”
Kubernetes (or K8s) is another tool for orchestrating containerized apps in a cluster. It’s job is to find the right place for a container, fulfill its desired state (e.g. “running, 5 replicas”), provide a network, internal IP, possibly, access from outside, apply updates, etc. Originally developed by Google, now Kubernetes is open source. Continue reading “What exactly is Kubernetes”