Posts tagged haskell

No, dynamic type systems are not inherently more open

Internet debates about typing disciplines continue to be plagued by a pervasive myth that dynamic type systems are inherently better at modeling “open world” domains. The argument usually goes like this: the goal of static typing is to pin everything down as much as possible, but in the real world, that just isn’t practical. Real systems should be loosely coupled and worry about data representation as little as possible, so dynamic types lead to a more robust system in the large.

This story sounds compelling, but it isn’t true. The flaw is in the premise: static types are not about “classifying the world” or pinning down the structure of every value in a system. The reality is that static type systems allow specifying exactly how much a component needs to know about the structure of its inputs, and conversely, how much it doesn’t. Indeed, in practice static type systems excel at processing data with only a partially-known structure, as they can be used to ensure application logic doesn’t accidentally assume too much.

Parse, don’t validate

Historically, I’ve struggled to find a concise, simple way to explain what it means to practice type-driven design. Too often, when someone asks me “How did you come up with this approach?” I find I can’t give them a satisfying answer. I know it didn’t just come to me in a vision—I have an iterative design process that doesn’t require plucking the “right” approach out of thin air—yet I haven’t been very successful in communicating that process to others.

However, about a month ago, I was reflecting on Twitter about the differences I experienced parsing JSON in statically- and dynamically-typed languages, and finally, I realized what I was looking for. Now I have a single, snappy slogan that encapsulates what type-driven design means to me, and better yet, it’s only three words long:

Parse, don’t validate.

Empathy and subjective experience in programming languages

A stereotype about programmers is that they like to think in black and white. Programmers like things to be good or bad, moral or immoral, responsible or irresponsible. Perhaps there is something romantic in the idea that programmers like to be as binary as the computers they program. Reductionist? Almost certainly, but hey, laugh at yourself a bit: we probably deserve to be made fun of from time to time.

Personally, I have no idea if the trope of the nuance-challenged programmer is accurate, but whether it’s a property of programmers or just humans behind a keyboard, the intensity with which we disagree with one another never ceases to amaze. Ask any group of working programmers what their least favorite programming language is, and there’s a pretty good chance things are going to get heated real fast. Why? What is it about programming that makes us feel so strongly that we are right and others are wrong, even when our experiences contradict those of tens or hundreds of thousands of others?

I think about that question a lot.

Demystifying MonadBaseControl

⦿ haskell

MonadBaseControl from the monad-control package is a confusing typeclass, and its methods have complicated types. For many people, it’s nothing more than scary, impossible-to-understand magic that is, for some reason, needed when lifting certain kinds of operations. Few resources exist that adequately explain how, why, and when it works, which sadly seems to have resulted in some FUD about its use.

There’s no doubt that the machinery of MonadBaseControl is complex, and the role it plays in practice is often subtle. However, its essence is actually much simpler than it appears, and I promise it can be understood by mere mortals. In this blog post, I hope to provide a complete survey of MonadBaseControl—how it works, how it’s designed, and how it can go wrong—in a way that is accessible to anyone with a firm grasp of monads and monad transformers. To start, we’ll motivate MonadBaseControl by reinventing it ourselves.

An opinionated guide to Haskell in 2018

⦿ haskell

For me, this month marks the end of an era in my life: as of February 2018, I am no longer employed writing Haskell. It’s been a fascinating two years, and while I am excitedly looking forward to what I’ll be doing next, it’s likely I will continue to write Haskell in my spare time. I’ll probably even write it again professionally in the future.

In the meantime, in the interest of both sharing with others the small amount of wisdom I’ve gained and preserving it for my future self, I’ve decided to write a long, rather dry overview of a few select parts of the Haskell workflow I developed and the ecosystem I settled into. This guide is, as the title notes, opinionated—it is what I used in my day-to-day work, nothing more—and I don’t claim that anything here is the only way to write Haskell, nor even the best way. It is merely what I found helpful and productive. Take from it as much or as little as you’d like.

Hackett progress report: documentation, quality of life, and snake

⦿ hackett, racket, haskell

Three months ago, I wrote a blog post describing my new, prototype implementation of my programming language, Hackett. At the time, some things looked promising—the language already included algebraic datatypes, typeclasses, laziness, and even a mini, proof of concept web server. It was, however, clearly still rather rough around the edges—error messages were poor, features were sometimes brittle, the REPL experience was less than ideal, and there was no documentation to speak of. In the time since, while the language is still experimental, I have tackled a handful of those issues, and I am excited to announce the first (albeit quite incomplete) approach to Hackett’s documentation.

I’d recommend clicking that link above and at least skimming around before reading the rest of this blog post, as its remainder will describe some of the pieces that didn’t end up in the documentation: the development process, the project’s status, a small demo, and some other details from behind the scenes.

Unit testing effectful Haskell with monad-mock

⦿ haskell, testing

Nearly eight months ago, I wrote a blog post about unit testing effectful Haskell code using a library called test-fixture. That library has served us well, but it wasn’t as easy to use as I would have liked, and it worked better with certain patterns than others. Since then, I’ve learned more about Haskell and more about testing, and I’m pleased to announce that I am releasing an entirely new testing library, monad-mock.

Realizing Hackett, a metaprogrammable Haskell

Almost five months ago, I wrote a blog post about my new programming language, Hackett, a fanciful sketch of a programming language from a far-off land with Haskell’s type system and Racket’s macros. At that point in time, I had a little prototype that barely worked, that I barely understood, and was a little bit of a technical dead-end. People saw the post, they got excited, but development sort of stopped.

Then, almost two months ago, I took a second stab at the problem in earnest. I read a lot, I asked a lot of people for help, and eventually I got something sort of working. Suddenly, Hackett is not only real, it’s working, and you can try it out yourself!

Lifts for free: making mtl typeclasses derivable

⦿ haskell

Perhaps the most important abstraction a Haskell programmer must understand to effectively write modern Haskell code, beyond the level of the monad, is the monad transformer, a way to compose monads together in a limited fashion. One frustrating downside to monad transformers is a proliferation of lifts, which explicitly indicate which monad in a transformer “stack” a particular computation should run in. Fortunately, the venerable mtl provides typeclasses that make this lifting mostly automatic, using typeclass machinery to insert lift where appropriate.

Less fortunately, the mtl approach does not actually eliminate lift entirely, it simply moves it from use sites to instances. This requires a small zoo of extraordinarily boilerplate-y instances, most of which simply implement each typeclass method using lift. While we cannot eliminate the instances entirely without somewhat dangerous techniques like overlapping instances, we can automatically derive them using features of modern GHC, eliminating the truly unnecessary boilerplate.

Rascal is now Hackett, plus some answers to questions

Since I published my blog post introducing Rascal, I’ve gotten some amazing feedback, more than I had ever anticipated! One of the things that was pointed out, though, is that Rascal is a language that already exists. Given that the name “Rascal” came from a mixture of “Racket” and “Haskell”, I always had an alternative named planned, and that’s “Hackett”. So, to avoid confusion as much as possible, Rascal is now known as Hackett.

With that out of the way, I also want to answer some of the other questions I received, both to hopefully clear up some confusion and to have something I can point to if I get the same questions in the future.