Disposable Local Development Environments with Vagrant, Docker, and Arkade

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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DevOps, SRE, and Platform Engineering

I compiled this thread on Twitter, and all of a sudden, it got quite some attention. So here, I'll try to elaborate on the topic a bit more. Maybe it would be helpful for someone trying to make a career decision or just improve general understanding of the most hyped titles in the industry.

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My Choice of Programming Languages

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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Making Sense Out Of Cloud-Native Buzz

I've been trying to wrap my head around the tremendous growth of the cloud-native zoo for quite some time. But recently I was listening to a wonderful podcast episode with the Linkerd creator Thomas Rampelberg and he kindly reminded me one thing about... microservices. Long story short, despite the common belief that microservices solve technical problems, the most appealing part of the microservice architecture apparently has something to do with solving organisational problems such as allocating teams to development areas or tackling software modernisation campaigns. And on the contrary, while helping with the org problems, microservices rather create new technical challenges!

Disclaimer: This article is not about Microservices vs Monolith.

That made me rethink the need for all those projects constituting the cloud-native landscape. From now on I can't help but see an awful load of projects solving all kinds of technical problems originated by the transition to the microservice paradigm.

Cloud Native landscape.

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API Developers Never REST

Disclaimer: despite the controversial title, this article is not trying to show that RPC is a superior approach to REST, or GraphQL is superior to RPC. Instead, the goal of the article is to give you an overview of the approaches, their strengths and weaknesses. The final choice anyway will be a trade-off.

Even though HTTP is an application layer (i.e. L7), protocol, when it comes to API development, HTTP de facto plays the role of a lower-level transport mechanism.

There are multiple conceptually different approaches on how to implement an API on top of HTTP:

  • REST
  • RPC
  • GraphQL

...but the actual list of things an average API developer needs to be aware of is not limited by these three dudes. There are also JSON, gRPC, protobuf, and many other terms in the realm. Let's try to sort them out, once and for all!

What is REST? What is RPC? What is GraphQL? What is the difference between REST, RPC, and GraphQL?

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