All articles on Programming

I strive to produce concise but readable code. One of my favorite tactics - minimizing the number of local variables - usually can be achieved through minting or discovering higher-level abstractions and joining them together in a more or less declarative way.

Thus, when writing Go code, I often utilize io.Reader and io.Writer interfaces and the related io machinery. A function like io.Copy(w io.Writer, r io.Reader) can be a perfect example of such a higher-level abstraction - it's a succinct at devtime and efficient at runtime way to move some bytes from the origin r to the destination w.

But conciseness often comes at a price, especially in the case of I/O handling - getting a sneak peek at the data that's being passed around becomes much trickier in the code that relies on the Reader and Writer abstractions.

So, is there an easy way to see the data that comes out of readers or goes into writers without a significant code restructure or an introduction of temporary variables?

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The official Kubernetes Go client comes loaded with high-level abstractions - Clientset, Informers, Cache, Scheme, Discovery, oh my! When I tried to use it without learning the moving parts first, I ran into an overwhelming amount of new concepts. It was an unpleasant experience, but more importantly, it worsened my ability to make informed decisions in the code.

So, I decided to unravel client-go for myself by taking a thorough look at its components.

But where to start? Before dissecting client-go itself, it's probably a good idea to understand its two main dependencies - k8s.io/api and k8s.io/apimachinery modules. It'll simplify the main task, but that's not the only benefit. These two modules were factored out for a reason - they can be used not only by clients but also on the server-side or by any other piece of software dealing with Kubernetes objects.

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Go standard library makes it super easy to start an HTTP server:

package main

import "net/http"

func main() {
    http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {
        w.Write([]byte("Hello there!\n"))
    })

    http.ListenAndServe(":8080", nil)
}

...or send an HTTP request:

package main

import "net/http"

func main() {
    resp, err := http.Get("http://example.com/")
    body, err := io.ReadAll(resp.Body)
}

In just ~10 lines of code, I can get a server up and running or fetch a real web page! In contrast, creating a basic HTTP server in C would take hundreds of lines, and anything beyond basics would require third-party libraries.

The Go snippets from above are so short because they rely on powerful high-level abstractions of the net and net/http packages. Go pragmatically chooses to optimize for frequently used scenarios, and its standard library hides many internal socket details behind these abstractions, making lots of default choices on the way. And that's very handy, but...

What if I need to fine-tune net/http sockets before initiating the communication? For instance, how can I set some socket options like SO_REUSEPORT or TCP_QUICKACK?

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I use a (rather oldish) MacBook for my day-to-day development tasks. But I prefer keeping my host operating system free of development stuff. This strategy has the following benefits:

• Increasing reproducibility of my code - it often happened to me in the past that some code worked on my machine but didn't work on others; usually, it was due to missing dependencies. Developing multiple projects on the same machine makes it harder to track what libraries and packages are required for what project. So, now I always try to have an isolated environment per project.
• Testing code on the target platform - most of my projects have something to do with server-side and infra stuff; hence the actual target platform is Linux. Since I use a MacBook, I spend a lot of time inside virtual machines running the same operating system as my servers do. So, I'd need to duplicate the development tools from my macOS on every Linux OS I happen to use.
• Keeping the host operating system clean and slim - even if I work on something platform-agnostic like a command-line tool, I prefer not to pollute my workstation with the dev tools and packages anyway. Projects and domains change often, and installing all the required stuff right into the host operating system would make it messy real quick.
• Decreasing time to recover in case of machine loss - a single multi-purpose machine quickly becomes a snowflake host. Coming up with the full list of things to reinstall in the case of a sudden machine loss would be hardly feasible.

Since I usually work on several projects at the same time, I need not one but many isolated development environments. And every environment should be project-tailored, easy to spin up, suspend, and, eventually, dispose. I figured a way to achieve that by using only a few tools installed on my host operating system, and I'm going to share it here.

The approach may be helpful for folks using macOS or Linux:

• to work on server-side and full-stack projects
• to do Linux systems programming
• to play with Cloud Native stack
• to build some cool command-line tools.

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When I was a kid, I used to spend days tinkering with woodworking tools. I was lucky enough to have a wide set of tools at my disposal. However, there was no one around to give me a hint about what tool to use when. So, I quickly came up with a heuristic: if my fingers and a tool survived an exercise, I've used the right tool; if either the fingers or the tool got damaged, I'd try other tools for the same task until I find the right one. And it worked quite well for me! Since then, I'm an apologist of the idea that every tool is good only for a certain set of tasks.

A programming language is yet another kind of tool. When I became a software developer, I adapted my heuristic to the new reality: if, while solving a task using a certain language, I suffer too much (fingers damage) or I need to hack things more often than not (tool damage), it's a wrong choice of a language.

Since the language is just a tool, my programming toolbox is defined by the tasks I work on the most often. Since 2010, I've worked in many domains, starting from web UI development and ending with writing code for infrastructure components. I find pleasure in being a generalist (jack of all trades), but there is always a pitfall of spreading yourself too thin (master of none). So, for the past few years, I've been trying to limit my sphere of expertise with the server-side, distributed systems, and infrastructure. Hence, the following choice of languages.

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