All of my previous articles have discussed basic language features and concepts in Haskell. My hope has been to provide new Haskellers with suitable starting material to help them get started. Even just as a language, Haskell is complex. There are many technical challenges and paradigm shifts one has to overcome to learn it. This is especially true for those coming from imperative languages.

However, this article will focus on something equally important. It’s no secret that most people consider Haskell not just a difficult language to learn technically, but an intimidating language. It has undoubted psychological hurdles. People seem to give up on Haskell at a higher rate than most other languages. By naming these problems, we can overcome them.

An Academic Language

People have long considered Haskell primarily as a research language. It builds on the lambda calculus, possibly the simplest, purest programming language. This gives it a host of connections to cool concepts in abstract mathematics, primarily the province of professors and PhD. students. The connection is so elegant many mathematical ideas can be well represented in Haskell.

But this connection has a price in accessibility. Important Haskell concepts include functors, monads, categories, etc. These are cool, but few without a math degree have any intuition for what the terms mean. Compare these to terms from other languages: class, iterator, loop, template. These terms are a lot more intuitive, so the languages using them have an automatic advantage in accessibility.

Aside from this terminology point though, the great academic interest is a good thing, not a bad thing. However, on the production side of things, the tooling has not been sufficient. It was simply too difficult to maintain a large-scale Haskell project. As a result, companies had little incentive to use it. This meant there was little to no balance of the academic influence.

Knowledge Distribution

The net result of Haskell’s academic primacy is a skewed knowledge base. The nature of academia is relatively few people spend a large amount of time on a relatively small set of problems. Consider another academic field, like virology. You have some experts who understand viruses at an extremely high level, and the rest of us know quite little. There are no hobbyist virologists. Unfortunately, this kind of distribution is not conducive to introducing new people to a topic.

Naturally, people have to learn from those who know more than they do. But the truth is they don’t want their teachers to be too much better. It helps tremendously to learn from someone who was in your shoes not too long ago. They’ll more likely remember the pitfalls and frustrations they encountered early on, so they’ll be able to help you avoid them. But when the distribution skews towards the extreme, there is no middle class. There are fewer people who can optimally teach new learners. In addition to not remembering old mistakes, experts tend to use overly complicated terminology. New folks may feel intimidated by this and despair.

Turning the Tide of Production

The lack of production work mentioned above contributes substantially to this divide. Many other languages, like C++, have strong academic followings. But since so many companies use C++ in production, it does not face the knowledge distribution problem Haskell does. Companies using C++ have no choice but to train people to use the language. Many of these people stick with the language long enough to train the next generation. This creates a more normal looking knowledge distribution curve.

The good news for Haskell is there have been major tooling improvements in the last few years. This has brought about a renaissance for the language. More companies are starting to use it in production. More meetups are happening; more people are writing libraries for the most crucial tasks. If this trend continues, Haskell will hopefully reach a tipping point, normalizing the knowledge distribution curve.

The Key Insight

If you are someone who is interested in learning Haskell, or who has tried learning Haskell in the past, there is one key thing to know. While the abstract mathematics is cool, understanding it is mostly unnecessary. Monads are essential, there is no denying. But category theory is overkill for most day-to-day problems you’ll solve. The dozens of language extensions might seem intimidating, but you can pick them up one-by-one as you go.

At the last Haskell eXchange, Don Stewart from Standard Chartered gave a talk about the company’s use of Haskell. He explained they rarely use anything outside of vanilla Haskell constructs*. They just don’t need to. Anything you can do with, say, lenses, you can accomplish without them.

Haskell is different from most other programming languages. It constrains you in ways those languages do not. But the constraints are not nearly as binding as they seem. You can’t use for loops. So use recursion. You can’t re-assign variables. So create new names for expressions. You just have take it one step at a time.

Taking Action

If this has inspired you to get started with Haskell, check out this checklist to learn the tools you need to get started.

If you’re already familiar with the basics and want to take the next step up, you should take a look at our workbook. You’ll learn about recursion and get to take a shot at 10 practice problems to test your skills!

Note

*At least with respect to their normal Haskell code. Some of their code is in a proprietary language of theirs called Mu, which is built on Haskell but obviously different.