Haskell is most likely quite different from any language you've ever used before. Compared to the usual set of concepts in a programmer's mental toolbox, functional programming offers us a profoundly different way to think about software.

In Haskell, we de-emphasise code that modifies data. Instead, we focus on functions that take immutable values as input and produce new values as output. Given the same inputs, these functions always return the same results. This is a core idea behind functional programming.

Along with not modifying data, our Haskell functions usually don't talk to the external world; we call these functions pure. We make a strong distinction between pure code and the parts of our programs that read or write files, communicate over network connections, or make robot arms move. This makes it easier to organize, reason about, and test our programs.

We abandon some ideas that might seem fundamental, such as having a for loop built into the language. We have other, more flexible, ways to perform repetitive tasks.

Even the way in which we evaluate expressions is different in Haskell. We defer every computation until its result is actually needed: Haskell is a lazy language. Laziness is not merely a matter of moving work around: it profoundly affects how we write programs.

Throughout this book, we will show you how Haskell's alternatives to the features of traditional languages are powerful, flexible, and lead to reliable code. Haskell is positively crammed full of cutting edge ideas about how to create great software.

Since pure code has no dealings with the outside world, and the data it works with is never modified, the kinds of nasty surprise in which one piece of code invisibly corrupts data used by another are very rare. Whatever context we use a pure function in, it will behave consistently.

Pure code is easier to test than code that deals with the outside world. When a function only responds to its visible inputs, we can easily state properties of its behavior that should always be true. We can automatically test that those properties hold for a huge body of random inputs, and when our tests pass, we move on. We still use traditional techniques to test code that must interact with files, networks, or exotic hardware. Since there is much less of this impure code than we would find in a traditional language, we gain much more assurance that our software is solid.

Lazy evaluation has some spooky effects. Let's say we want to find the k least-valued elements of an unsorted list. In a traditional language, the obvious approach would be to sort the list and take the first k elements, but this is expensive. For efficiency, we would instead write a special function that takes these values in one pass, and it would have to perform some moderately complex book-keeping. In Haskell, the sort-then-take approach actually performs well: laziness ensures that the list will only be sorted enough to find the k minimal elements.

Better yet, our Haskell code that operates so efficiently is tiny, and uses standard library functions.

-- file: ch00/KMinima.hs
-- lines beginning with "--" are comments.

minima k xs = take k (sort xs)

It can take a while to develop an intuitive feel for when lazy evaluation is important, but when we exploit it, the resulting code is often clean, brief, and efficient.

As the above example shows, an important aspect of Haskell's power lies in the compactness of the code we write. Compared to working in popular traditional languages, when we develop in Haskell we often write much less code, in substantially less time, and with fewer bugs.

We believe that it is easy to pick up the basics of Haskell programming, and that you will be able to successfully write small programs within a matter of hours or days.

Since effective programming in Haskell differs greatly from other languages, you should expect that mastering both the language itself and functional programming techniques will require plenty of thought and practice.

Harking back to our own days of getting started with Haskell, the good news is that the fun begins early: it's simply an entertaining challenge to dig into a new language, in which so many commonplace ideas are different or missing, and to figure out how to write simple programs.

For us, the initial pleasure lasted as our experience grew and our understanding deepened. In other languages, it's difficult to see any connection between science and the nuts-and-bolts of programming. In Haskell, we have imported some ideas from abstract mathematics and put them to work. Even better, we find that not only are these ideas easy to pick up, they have a practical payoff in helping us to write more compact, reusable code.

Furthermore, we won't be putting any “brick walls” in your way: there are no especially difficult or gruesome techniques in this book that you must master in order to be able to program effectively.

That being said, Haskell is a rigorous language: it will make you perform more of your thinking up front. It can take a little while to adjust to debugging much of your code before you ever run it, in response to the compiler telling you that something about your program does not make sense. Even with years of experience, we remain astonished and pleased by how often our Haskell programs simply work on the first try, once we fix those compilation errors.

What will you need to know before reading this book? We expect that you already know how to program, but if you've never used a functional language, that's fine.

No matter what your level of experience is, we have tried to anticipate your needs: we go out of our way to explain new and potentially tricky ideas in depth, usually with examples and images to drive our points home.

As a new Haskell programmer, you'll inevitably start out writing quite a bit of code by hand for which you could have used a library function or programming technique, had you just known of its existence. We've packed this book with information to help you to come up to speed as quickly as possible.

Of course, there will always be a few bumps along the road. If you start out anticipating an occasional surprise or difficulty along with the fun stuff, you will have the best experience. Any rough patches you might hit won't last long.

As you become a more seasoned Haskell programmer, the way that you write code will change. Indeed, over the course of this book, the way that we present code will evolve, as we move from the basics of the language to increasingly powerful and productive features and techniques.

Languages that use simple static type systems have been the mainstay of the programming world for decades. Haskell is statically typed, but its notion of what types are for, and what we can do with them, is much more flexible and powerful than traditional languages. Types make a major contribution to the brevity, clarity, and efficiency of Haskell programs.

Although powerful, Haskell's type system is often also unobtrusive. If we omit explicit type information, a Haskell compiler will automatically infer the type of an expression or function. Compared to traditional static languages, to which we must spoon-feed large amounts of type information, the combination of power and inference in Haskell's type system significantly reduces the clutter and redundancy of our code.

Several of Haskell's other features combine to further increase the amount of work we can fit into a screenful of text. This brings improvements in development time and agility: we can create reliable code quickly, and easily refactor it in response to changing requirements.

Sometimes, Haskell programs may run more slowly than similar programs written in C or C++. For most of the code we write, Haskell's large advantages in productivity and reliability outweigh any small performance disadvantage.

Multicore processors are now ubiquitous, but they remain notoriously difficult to program using traditional techniques. Haskell provides unique technologies to make multicore programming more tractable. It supports parallel programming, software transactional memory for reliable concurrency, and scales to hundreds of thousands of concurrent threads.

Over the past decade, dynamically typed, interpreted languages have become increasingly popular. They offer substantial benefits in developer productivity. Although this often comes at the cost of a huge performance hit, for many programming tasks productivity trumps performance, or performance isn't a significant factor in any case.

Brevity is one area in which Haskell and dynamically typed languages perform similarly: in each case, we write much less code to solve a problem than in a traditional language. Programs are often around the same size in dynamically typed languages and Haskell.

When we consider runtime performance, Haskell almost always has a huge advantage. Code compiled by the Glasgow Haskell Compiler (GHC) is typically between 20 and 60 times faster than code run through a dynamic language's interpreter. GHC also provides an interpreter, so you can run scripts without compiling them.

Another big difference between dynamically typed languages and Haskell lies in their philosophies around types. A major reason for the popularity of dynamically typed languages is that only rarely do we need to explicitly mention types. Through automatic type inference, Haskell offers the same advantage.

Beyond this surface similarity, the differences run deep. In a dynamically typed language, we can create constructs that are difficult to express in a statically typed language. However, the same is true in reverse: with a type system as powerful as Haskell's, we can structure a program in a way that would be unmanageable or infeasible in a dynamically typed language.

It's important to recognise that each of these approaches involves tradeoffs. Very briefly put, the Haskell perspective emphasises safety, while the dynamically typed outlook favours flexibility. If someone had already discovered one way of thinking about types that was always best, we imagine that everyone would know about it by now.

Of course, we have our own opinions about which tradeoffs are more beneficial. Two of us have years of experience programming in dynamically typed languages. We love working with them; we still use them every day; but usually, we prefer Haskell.

Here are just a few examples of large software systems that have been created in Haskell. Some of these are open source, while others are proprietary products.

is a sample of some of the companies using Haskell in late 2008, taken from the Haskell wiki.

For practical work, almost as important as a language itself is the ecosystem of libraries and tools around it. Haskell has a strong showing in this area.

The most widely used compiler, GHC, has been actively developed for over 15 years, and provides a mature and stable set of features.

The GHC compiler ships with a collection of useful libraries. Here are a few of the common programming needs that these libraries address.

The Hackage package database is the Haskell community's collection of open source libraries and applications. Most libraries published on Hackage are licensed under liberal terms that permit both commercial and open source use. Some of the areas covered by open source libraries include the following.

A few decades before modern computers were invented, the mathematician Alonzo Church developed a language called the lambda calculus. He intended it as a tool for investigating the foundations of mathematics. The first person to realize the practical connection between programming and the lambda calculus was John McCarthy, who created Lisp in 1958.

During the 1960s, computer scientists began to recognise and study the importance of the lambda calculus. Peter Landin and Christopher Strachey developed ideas about the foundations of programming languages: how to reason about what they do (operational semantics) and how to understand what they mean (denotational semantics).

In the early 1970s, Robin Milner created a more rigorous functional programming language named ML. While ML was developed to help with automated proofs of mathematical theorems, it gained a following for more general computing tasks.

The 1970s saw the emergence of lazy evaluation as a novel strategy. David Turner developed SASL and KRC, while Rod Burstall and John Darlington developed NPL and Hope. NPL, KRC and ML influenced the development of several more languages in the 1980s, including Lazy ML, Clean, and Miranda.

By the late 1980s, the efforts of researchers working on lazy functional languages were scattered across more than a dozen languages. Concerned by this diffusion of effort, a number of researchers decided to form a committee to design a common language. After three years of work, the committee published the Haskell 1.0 specification in 1990. It named the language after Haskell Curry, an influential logician.

Many people are rightfully suspicious of “design by committee”, but the work of the Haskell committee is a beautiful example of the best work a committee can do. They produced an elegant, considered language design, and succeeded in unifying the fractured efforts of their research community. Of the thicket of lazy functional languages that existed in 1990, only Haskell is still actively used.

Since its publication in 1990, the Haskell language standard has seen five revisions, most recently in 1998. A number of Haskell implementations have been written, and several are still actively developed.

During the 1990s, Haskell served two main purposes. On one side, it gave language researchers a stable language in which to experiment with making lazy functional programs run efficiently. Other researchers explored how to construct programs using lazy functional techniques. Still others used it as a teaching language.

While these basic explorations of the 1990s proceeded, Haskell remained firmly an academic affair. The informal slogan of those inside the community was to “avoid success at all costs”. Few outsiders had heard of the language at all. Indeed, functional programming as a field was quite obscure.

During this time, the mainstream programming world experimented with relatively small tweaks: from programming in C, to C++, to Java. Meanwhile, on the fringes, programmers were beginning to tinker with new, more dynamic languages. Guido van Rossum designed Python; Larry Wall created Perl; and Yukihiro Matsumoto developed Ruby.

As these newer languages began to seep into wider use, they spread some crucial ideas. The first was that programmers are not merely capable of working in expressive languages; in fact, they flourish. The second was in part a byproduct of the rapid growth in raw computing power of that era: it's often smart to sacrifice some execution performance in exchange for a big increase in programmer productivity. Finally, several of these languages borrowed from functional programming.

Over the past half a decade, Haskell has successfully escaped from academia, buoyed in part by the visibility of Python, Ruby, and even Javascript. The language now has a vibrant and fast-growing culture of open source and commercial users, and researchers continue to use it to push the boundaries of performance and expressiveness.

If you're looking for a Haskell library to use for a particular task, or an application written in Haskell, check out the following resources.

There are a number of ways you can get in touch with other Haskell programmers, to ask questions, learn what other people are talking about, and simply do some social networking with your peers.

I had a great deal of fun working with John and Don. Their independence, good nature, and formidable talent made the writing process remarkably smooth.

Simon Peyton Jones took a chance on a college student who emailed him out of the blue in early 1994. Interning for him over that summer remains a highlight of my professional life. With his generosity, boundless energy, and drive to collaborate, he inspires the whole Haskell community.

My children, Cian and Ruairi, always stood ready to help me to unwind with wonderful, madcap little-boy games.

Finally, of course, I owe a great debt to my wife, Shannon, for her love, wisdom, and support during the long gestation of this book.

I am so glad to be able to work with Bryan and Don on this project. The depth of their Haskell knowledge and experience is amazing. I enjoyed finally being able to have the three of us sit down in the same room -- over a year after we started writing.

My 2-year-old Jacob, who decided that it would be fun to use a keyboard too, and is always eager to have me take a break from the computer and help him make some fun typing noises on a 50-year-old Underwood typewriter.

Most importantly, I wouldn't have ever been involved in this project without the love, support, and encouragement from my wife, Terah.

Before all else, I'd like to thank my amazing co-conspirators, John and Bryan, for encouragment, advice and motivation.

My colleagues at Galois, Inc., who daily wield Haskell in the real world, provided regular feedback and war stories, and helped ensured a steady supply of espresso.

My PhD supervisor, Manuel Chakravarty, and the PLS research group, who provided encouragement, vision and energy, and showed me that a rigorous, foundational approach to programming can make the impossible happen.

And, finally, thanks to Suzie, for her insight, patience and love.

We developed this book in the open, posting drafts of chapters to our web site as we completed them. Readers then submitted feedback using a web application that we developed. By the time we finished writing the book, about 800 people had submitted over 7,500 comments, an astounding figure.

We deeply appreciate the time that so many people volunteered to help us to improve our book. Their encouragement and enthusiasm over the 15 months we spent writing made the process a pleasure.

The breadth and depth of the comments we received have profoundly improved the quality of this book. Nevertheless, all errors and omissions are, of course, ours.

The following people each contributed over 1% of the total number of review comments that we received. We would like to thank them for their care in providing us with so much detailed feedback.

Alex Stangl, Andrew Bromage, Brent Yorgey, Bruce Turner, Calvin Smith, David Teller, Henry Lenzi, Jay Scott, John Dorsey, Justin Dressel, Lauri Pesonen, Lennart Augustsson, Luc Duponcheel, Matt Hellige, Michael T. Richter, Peter McLain, Rob deFriesse, Rüdiger Hanke, Tim Chevalier, Tim Stewart, William N. Halchin.

We are also grateful to the people below, each of whom contributed at least 0.2% of all comments.

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We would like to acknowledge the following people, many of whom submitted a number of comments.

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Finally, we wish to thank those readers who submitted over 800 comments anonymously.