All articles on Programming

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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I've been working on my new Rust side-project for several months now, and I've got some learnings to share. The project is called pq - it's a command-line tool to parse and query log files as time series. It comes with its own domain-specific language that is highly influenced by PromQL. A typical pq usage may look like this:

tail -f /var/log/nginx/access.log | pq '
/...some fancy regex.../
| map {
    .0 as ip,
    .1:ts,
    .2 as method,
    .3:str as status_code,
    .4 as content_len
  }
| select topk(
      10,
      sum(
          count_over_time(
              __line__{method="GET", status_code="200"}[1s]
          )
      ) by (ip)
  )
| to_json
'

pq has many components, including various log parsing strategies and a pretty sophisticated query execution engine. But surprisingly or not, about half of the time I've put into this project so far was dedicated to writing the parser of the pq's own query language. To be honest, when I was starting the project, I didn't see that coming...

Luckily, writing a parser in Rust was mostly a pleasant experience, thanks to a crate concisely named nom. Although learning how to write parsers with nom wasn't completely seamless. So here is my journey.

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I often find myself staring at Nginx or Envoy access logs flooding my screens with real-time data. My only wish at such moments is to be able to aggregate these lines somehow and analyze the output at a slower pace, ideally, with some familiar and concise query language. And to my surprise, I haven't met a tool satisfying all my requirements yet. Well, I should be honest here - I haven't done thorough research. But if there would be a tool as widely known as jq for JSON, I wouldn't miss it probably.

So, here we go - my attempt to write a full-fledged parsing and query engine and master Rust at the same time. Yes, I know, it's a bad idea. But who has time for good ones?

First things first - a usage preview:

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I spent the last couple of months writing code in Rust. It was probably my third or fourth attempt to write something substantial in this language. And every time my level of understanding of things deepened. I'm by no means a Rust expert so probably I'll be extremely inaccurate in the terminology here. And likely I'll get lots of technical details wrong too. But I had this epiphany moment of how the visibility and privacy model works in Rust so I can't help but think of sharing it with someone else.

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