Book Image

Soar with Haskell

By : Tom Schrijvers
Book Image

Soar with Haskell

By: Tom Schrijvers

Overview of this book

With software systems reaching new levels of complexity and programmers aiming for the highest productivity levels, software developers and language designers are turning toward functional programming because of its powerful and mature abstraction mechanisms. This book will help you tap into this approach with Haskell, the programming language that has been leading the way in pure functional programming for over three decades. The book begins by helping you get to grips with basic functions and algebraic datatypes, and gradually adds abstraction mechanisms and other powerful language features. Next, you’ll explore recursion, formulate higher-order functions as reusable templates, and get the job done with laziness. As you advance, you’ll learn how Haskell reconciliates its purity with the practical need for side effects and comes out stronger with a rich hierarchy of abstractions, such as functors, applicative functors, and monads. Finally, you’ll understand how all these elements are combined in the design and implementation of custom domain-specific languages for tackling practical problems such as parsing, as well as the revolutionary functional technique of property-based testing. By the end of this book, you’ll have mastered the key concepts of functional programming and be able to develop idiomatic Haskell solutions.
Table of Contents (23 chapters)
Free Chapter
1
Part 1:Basic Functional Programming
6
Part 2: Haskell-Specific Features
11
Part 3: Functional Design Patterns
16
Part 4: Practical Programming

Call by Need

Haskell’s evaluation strategy is called Call by Need or lazy evaluation. It is quite similar to Call by Name in that it only evaluates work that is needed for the result of the computation. At the same time, it avoids the main problem of Call by Name: it does not duplicate any work.

Sharing

The way in which lazy evaluation avoids duplication is known as sharing, or sometimes also as memoization. Instead of duplicating work, the work is shared, and when the work is performed once, all who share it can use the work’s results without redoing them.

Conceptually, we model sharing the work by using let binding:

  (\x -> x + x) (sin 1.0)
↣ let w = sin 1.0
   in w + w

To evaluate the sum in the body of the let binding, we first have to evaluate its left operand. As this operand is a let bound variable w, we consult the binding. The binding shows that the variable is bound to a reducible expression. Hence, we reduce...