Haskell is to math as Perl is to English?

Fortran superficially looks like mathematics. Its name comes from “FORmula TRANslation,” and the language does provide a fairly straightforward way to turn formulas into code. But the similarity to mathematics ends there at the lowest level of abstraction.

Haskell, on the other hand, is more deeply mathematical. Fortran resembles math in the small, but Haskell resembles math in the large. Haskell has mathematical structures at a higher level of abstraction than just formulas. As a recent article put it

While Fortran provides a comfortable translation of mathematical formulas, Haskell code begins to resemble mathematics itself.

On its surface, Perl is one of the least English-like programming languages. It is often criticized as looking like “line noise” because of its heavy use of operators. By contrast, Python has been called “executable pseudocode” because the source is easier to read, particularly for novice programmers. And yet at a deeper level, Perl is more English-like than other programming languages such as Python.

Larry Wall explains in Natural Language Principles in Perl that he designed Perl to incorporate features of spoken language. For example, Perl has analogs of articles and pronouns. (Larry Wall studied both linguistics and computer science in college.) Opinions differ on how well his experiment with incorporating natural language features into programming languages has worked out, but it was an interesting idea.

Related posts

Haskell / Category theory decoder ring

Haskell uses a lot of ideas from category theory, but the correspondence between Haskell and category theory can be a little hard to see at times.

One difficulty is that although Haskell articles use terms like functor and monad from category theory, they seldom actually talk about categories per se. If we’ve got functors, where are the categories? (This reminds me of Darth Vader asking “If this is a consular ship, where is the ambassador?”)

In Haskell literature, everything implicitly lives Hask, the category of Haskell types, or in some subcategory of Hask. This means that the category itself is not the focus of attention. In category theory, functors often operate between very different classes of objects, such as topological spaces and their fundamental groups, and so it’s more important to state what category something lives in.

Another potential stumbling block is to think of Haskell types as categories and values as objects. That would be reasonable, since in computer science an “object” is an instance of a type. But the right correspondence is to think of Haskell types as categorical objects. Instances of types are below the level of abstraction we’re working at. This is analogous to how category theory treats objects as black boxes with no way to talk about what’s inside.

Finally, Haskell monads look a little different from categorical monads. Haskell’s return corresponds directly to unit, usually written as η, in category theory. But Haskell monads have a bind operator >>= while mathematical monads have a join operator μ. These are not equivalent, though you can implement each in terms of the other:

join :: Monad m => m (m a) -> m a
join x = x >>= id

(>>=) :: Monad m => m a -> (a -> m b) -> m b
x >>= f = join (fmap f x)

To read more along these lines, see the Wikibooks article on Haskell and Category theory.

Update: Stephen Diehl suggested I mention the differences between the idealized category Hask and the implementation of the Haskell language. These are discussed here.

Related: Applied category theory

Programming languages and magic

In the context of programming languages, “magic” is often a pejorative term for code that does something other than what it appears to do.

Programmers seem to have a love/hate relationship with magic. Even people who say that don’t like magic (e.g. because it’s hard to debug) end up using it. The Haskell community prides itself on having a transparent language with no magic, and yet monads are slightly magical. The whole purpose of a monad is to hide explicit data flow, though in a principled way. Haskell’s do notation is more magical, and templates are even more magical still. (However, I do hear some Haskellers express disdain for templates.)

People who like magic tend to use the word “automagic” instead. It means about the same thing as “magic” but with a positive connotation.

To conclude with a couple sweeping generalizations, magic fans tend to be tool-oriented (such as Microsoft developers) while magic detractors tend to be language-oriented (such as Haskell developers ).

Update: Someone asked me on Twitter about the difference between abstraction and magic. I’d say abstraction hides details, but magic is actively misleading or ironic.

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Monads are hard because …

Here’s a nice quip from Luke Gorrie on Twitter:

Monads are hard because there are so many bad monad tutorials getting in the way of finally finding Wadler’s nice paper.

Here’s the paper by Philip Wadler that I expect Luke Gorrie had in mind: Monads for functional programming.

Here’s the key line from Wadler’s paper:

Pure functional languages have this advantage: all flow of data is made explicit. And this disadvantage: sometimes it is painfully explicit.

That’s the problem monads solve: they let you leave implicit some of the repetitive code otherwise required by functional programming. That simple but critical point left out of many monad tutorials.

Dan Piponi wrote a blog post You Could Have Invented Monads! (And Maybe You Already Have) very much in the spirit of Wadler’s paper. He starts with the example of adding logging to a set of functions. You could have every function return not just the return value that you’re interested in but also the updated state of the log. This quickly gets messy. For example, suppose your basic math functions write to an error log if you call them with illegal arguments. That means your square root function, for example, has to take as input not just a real number but also the state of the error log before it was called. Monads give you a way of effectively doing the same thing, but conceptually separating the code that takes square roots from the code that maintains the error log.

As for “so many bad monad tutorials,” see Brent Yorgey on the monad tutorial fallacy.

By the way, this post is not Yet Another Monad Tutorial. It’s simply an advertisement for the tutorials by Philip Wadler and Dan Piponi.

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“Conventional” is relative

I found this line from Software Foundations amusing:

… we can ask Coq to “extract,” from a Definition, a program in some other, more conventional, programming language (OCaml, Scheme, or Haskell) with a high-performance compiler.

Most programmers would hardly consider OCaml, Scheme, or Haskell “conventional” programming languages, but they are conventional relative to Coq. As the authors said, these languages are “more conventional,” not “conventional.”

I don’t mean to imply anything negative about OCaml, Scheme, or Haskell. They have their strengths—I briefly mentioned the advantages of Haskell just yesterday—but they’re odd birds from the perspective of the large majority of programmers who work in C-like languages.

Real World Haskell

I’m reading Real World Haskell because one of my clients’ projects is written in Haskell. Some would say that “real world Haskell” is an oxymoron because Haskell isn’t used in the real world, as illustrated by a recent xkcd cartoon.

It’s true that Haskell accounts for a tiny portion of the world’s commercial software and that the language is more popular in research. (There would be no need to put “real world” in the title of a book on PHP, for example. You won’t find a lot of computer science researchers using PHP for its elegance and nice theoretical properties.) But people do use Haskell on real projects, particularly when correctness is a high priority.[1] In any case, Haskell is “real world” for me since one of my clients uses it. As I wrote about before, applied is in the eye of the client.

I’m not that far into Real World Haskell yet, but so far it’s just what I was looking for. Another book I’d recommend is Graham Hutton’s Programming in Haskell. It makes a good introduction to Haskell because it’s small (184 pages) and focused on the core of the language, not so much on “real world” complications.

A very popular introduction to Haskell is Learn You a Haskell for Great Good. I have mixed feelings about that one. It explains most things clearly and the informal tone makes it easy to read, but the humor becomes annoying after a while. It also introduces some non-essential features of the language up front that could wait until later or be left out of an introductory book.

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[1] Everyone would say that it’s important for their software to be correct. But in practice, correctness isn’t always the highest priority, nor should it be necessarily. As the probability of error approaches zero, the cost of development approaches infinity. You have to decide what probability of error is acceptable given the consequences of the errors.

It’s more important that the software embedded in a pacemaker be correct than the software that serves up this blog. My blog fails occasionally, but I wouldn’t spend $10,000 to cut the error rate in half. Someone writing pacemaker software would jump at the chance to reduce the probability of error so much for so little money.

On a related note, see Maybe NASA could use some buggy software.