Recently I started playing around with Traefik on Kubernetes. Though I started my cluster with Nginx as load-balancer handling Kubernetes’ ingresses, I quickly switched this one out with Traefik as I have a need for wildcard LetsEncrypt certificates. Requesting those with cert-manager is more difficult, and given Traefik comes with a long list of supported vendors for DNS validation, it was a fairly easy choice to go with them.
The switch from Nginx, with which I’ve been working for several years to now, to Traefik, from which I’ve only heard, has been fairly big. Combined with Kubernetes with which I’m totally unfamiliar, and it’s been an incredibly steep learning curve over the past few days.
As I have only been working with Kubernetes for a few days, most things just like Helm are pretty new to me, and as such I get lost a lot. One of the things I had to find out the hard is the way a Helm chart and it’s associated configuration options are resolved.
Kubernetes is a technology I have wanted to dive into for a long time, yet pushed off for over a year due to the complexity involved. There’s so much to learn about that I did not feel like doing so, up until now. In this post I’ll describe the steps I have taken to get something up and running, not so much as a complete guide to Kubernetes, but rather a quick list of resources with which you can get started.
If there is one thing I hate about web development it is creating input forms. It’s something I’m bad at, mostly because I do not have a cohesive mental model about how to deal with input props. In this post I’m about to explore some techniques to make form creation using React somewhat easier.
During a month long refactor session on the Skyhop back-end I had upgraded all of my dependencies to their latest versions. Later during this process I discovered there was a dependency incompatibility between EF Core 5 and SqlKata. I weighted my options, and decided it would be easier to revert back to EF Core 3, than to solve this dependency compatibility issue in another way.
Recently I have been looking for more flexible ways to search through text within a SQL database, and I stumbled upon a suggestion which indicated to use the so called Levenshtein distance. This parameter is a value which indicates the number of changes to a string required in order to match the searched value. In a certain way it is possible to regard this Levenshtein distance as being a similarity rating between two strings, whereas the lower the value, the more similar it is.
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