A year ago, I was stuck in my efforts to properly learn Clojure. So I decided to take a step back and work through SICP: slowly, but steadily. I sticked to it rather well, even finished an exercise on the day I moved. Neither did I take a break on Christmas. However, I had to stop in March, because working on the first project with two colleagues in our own company (Composed GmbH) was too much besides working two other jobs. With that project finished, and one of the two jobs quit, I have some time again.
Satisfied with SICP
I quickly took up SICP again in July to learn about lazy evaluation and streams, which is also used in Clojure. But now that I got out of it what I needed, I'm back to Clojure. Those nine months of SICP with Scheme and Racket really paid off. And since I'm not particularly interested in building my own LISP, I won't continue with SICP for the moment and leave the two last chapters unread… but I'll leave the bookmark where it is (almost at the end of the third chapter).
So what's up next? A lot of teaching from August to February (15 lessons a week) including a completely new course (on software testing), which takes up three days a week, plus some preparation. Then there's a lot to do for our own company; maybe project work, or building up some infrastructure and creating a proper website (probably using Hugo). But I'd also like to learn something new besides, and doing so systematically (again).
- Erlang and Elixir: Actually two (functional) languages both based on the same technology; the Erlang VM (Beam) and OTP. I'm very interested in Concurrency, and Go's CSP model was a revelation in that respect for me. Now I'd like to learn more about the Actor Model, and on how to build resilient applications. For practical purposes (e.g. building web applications), I'll use Elixir. But since Elixir is a hosted language, I should also spend some time with the host language Erlang.
- Clojure: In my opinion, this is the most beautiful language. The basic data structures (lists, vectors, maps, and sets) just feel right. There's a powerful and intuitive API for them, they can be made up with (nested) literals, and once you get the hang on persistent data structures, you don't want to go back. I already know the host language (Java), and this interoperability makes it a great choice for practical projects like web applications. I haven't used macros yet, so there's even more power lurking beneath. Rich Hickey created Clojure out of frustration with all the other languages. The result is a great pleasure to work with, even though I probably need to improve my workflow with Emacs in order to become really productive.
- Rust: Just in case Erlang/Elixir and Clojure should be too slow or too memory intensive for certain tasks, Rust will solve both issues. It's quite functional in some respects (iterators, enums, pattern matching) and, unlike the other languages mentioned here, strongly typed. However, its collections are basically mutable, and many operations on them only work if they are declared so. This is one step back from the persistent data structures of the other languages/platforms, but exactly what is needed for uncompromising performance.
With those three (or four) platforms (or languages), I'll cover a lot of ground. I did neglect languages suited for writing web frontends, but this is not my main concern. (And Elm would probably be my choice for it. Or maybe ClojureScript.)
But what should I do with these programming languages? Without a proper project, I won't stick to them and do something else.
For the lack of a productive project (with deadlines and payment, but also with the option to pivot to a language I'm already familiar with), I have to make up some kind of project.
There's a very big book at the very bottom of my bookshelf: Introduction to Algorithms (4th Edition), which I now own since its release in spring 2022. I wasn't able to read a single page of it yet, but now the time has come. Being more confident with academic computer science texts after my encounter with SICP, I'd like to tackle this one.
The algorithms are given as pseudo-code, so much I already figured out. I don't know yet if there are exercises, and whether or not they involve mathematical proofs. (I'm writing this during my holidays, away from my bookshelf.) However, I'd like to focus on the implementation, for which I only need to understand the algorithms, but not to proof their correctness.
Besides building up an arsenal of algorithms for different kinds of problems, I'd like to practice the programming languages mentioned above on those algorithms. But learning two new things—algorithms and languages—probably will be too hard. So here's my plan:
- Reading and Understanding: I'll probably need to read every section two or three times until I understand it. And I should also make some sketches and play through some examples using pen and paper. With that understanding, I'll go ahead with the implementation of the algorithm.
- Go: The pseudo-code descriptions are a good fit for the structured programming model of Go. I already know Go quite well, and it comes with all the tools needed to play with algorithms: Data structures (slices, maps, structs), relatively lean syntax, and a built-in test module with benchmarking capabilities, besides appropriate tooling. Using Go, I can focus on the algorithm and play with it, until I really understand it.
- Rust: Having a working version in Go, I can just translate it to Rust. There will be a few language-specific issues to deal with (immutability, the need for smart pointers when using recursive data structures), but that is exactly what I need to learn. Doing some runtime comparisons with Go will also be interesting.
- Erlang: Now towards functional programming: Having implemented an algorithm in two languages, I'll now translate it to a different paradigm. This will teach me the basics of Erlang. Making the algorithms work concurrently would also be a good exercise, for which I probably should first go back to Go (and Rust), which also yields interesting comparisons for the different concurrency models. (Elixir can wait for later.)
- Clojure: Having grasped the functional implementation of an algorithm, re-writing it in Clojure should amount to simplification and more concise code. (Some additional benchmarking with comparisons to other languages would also be interesting to finish off each algorithm.)
So this is quite some project, and I wonder for how long I'll be able to stick to it. I once again try to work on it every day, without having any goals in terms of finishing the book.
I'll put the code into my algorithms repository. I'll also write a diary, so that I at least have a commit every day, even if I just read and don't manage to write any code.