Customer Reviews

Purely Functional Data Structures

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Okasaki's slim volume is one of the best expositions on implementing data structures & algorithms in a functional language. After taking an introductory course on functional programming, this would be the book which tells you where to go next.

This book doesn't just present a rehash/rewrite of imperative data structures, only written in a functional language. Instead, Okasaki makes sure to emphasize benefits which only functional programming can bring to the table. For example, many functional data structures can compactly represent not just their current state, but all of their past states as well--a feature called "Persistence". Also, functional newbie programmers might be wondering why lazy vs. strict programming is a big deal, and Okasaki shows clearly where data structures can benefit from either being lazy or being strict.

For the advanced reader, Okasaki also presents several powerful techniques for analyzing the runtime of algorithms, including the so-called "Banker's Method" and the "Physicist's Method" for analyzing amortized algorithms.

I hope that Okasaki comes out with a 2nd edition of this book; there is one missing piece in particular which I really wish he would have included: Although he presents an EXTREMELY lucid description of how to implement Red-Black trees in a functional language, he only presented algorithms for insertion and querying. Of course, deletion from a red-black tree is the hardest part, left here, I suppose, as an exercise to the student. If you want to supply this missing piece yourself, check out a paper by Stefan Kars, "Red-black trees with types", J. Functional Programming 11(4):425-432, July, 2001. It presents deletion routines, but you'll still want to read Okasaki's book first, for unless you're very much smarter than me you won't be able to understand Kars' paper until you read Okasaki's exposition of red black trees.

Finally, this book is not just useful for programmers in functional languages; logic programmers, using prolog or a varient, will also find this book very helpful, because most of the techniques (all of the techniques, really, with the exception perhaps of the lazy programming stuff) can be directly applied in a prolog programming setting as well.

After reading this book and implementing some of the data structures for yourself, you'll be amazed at how fast algorithms can run, even when written in a functional language!

The description of the book says it includes source code in both ML and Haskell. Unfortunately, the body of the text uses ML exclusively, and the Haskell code is banished to an appendix.

I say "unfortunately", because many of the data structures used depend on lazy evaluation, which comes quite naturally to Haskell, and seems to require some sort of non-standard extension in ML.

While the content is good, I wish it would have used Haskell as the primary exposition language.

A correction to another review: Hash Tables are included, briefly.

[This review copied from my moribund blog at [...] ]

The typical data structures most programmers know and use require imperative programming: they fundamentally depend on replacing the values of fields with assignment statements, especially pointer fields. A particular data structure represents the state of something at that particular moment in time, and that moment only. If you want to know what the state was in the past you needed to have made a copy of the entire data structure back then, and kept it around until you needed it. (Alternatively, you could keep a log of changes made to the data structure that you could play in reverse until you get the previous state - and then play it back forwards to get back to where you are now. Both these techniques are typically used to implement undo/redo, for example.)

Or you could use a persistent data structure. A persistent data structure allows you to access previous versions at any time without having to do any copying. All you needed to do at the time was to save a pointer to the data structure. If you have a persistent data structure, your undo/redo implementation is simply a stack of pointers that you push a pointer onto after you make any change to the data structure.

This can be quite useful--but it is typically very hard to implement a persistent data structure in an imperative language, especially if you have to worry about memory management [1]. If you're using a functional programming language--especially a language with lazy semantics like Haskell--then all your data structures are automatically persistent, and your only problem is efficiency (and of course, in your functional languages, the language system takes care of memory management). But for practical purposes, as a hardcore C++ programmer for professional purposes, I was locked out of the world of persistent data structures.

Now, however, with C# and C++/CLI in use (and garbage collection coming to C++ any time now ... [2]) I can at last contemplate the use of persistent data structures in my designs. And that's great, because it gave me an excuse to take one of my favorite computer science books off the shelf and give it another read.

The book is Purely Functional Data Structures, by Chris Okasaki. I find it to be a very well written and easy to understand introduction to the design and analysis of persistent data structures--or equivalently--for the design and analysis of any data structure you'd want to use in a functional language.

There are two key themes of the book: First, to describe the use and implementation of several persistent data structures, such as different kinds of heaps, queues, and random-access lists, and second, to describe how to create your own efficient persistent data structures.

A nice feature here is the inclusion of "Hint to Practitioners" sidebars that point out which of these data structures work especially well in various contexts.

The second theme is the more demanding one--but of course, it is teaching something really valuable. First, the methods of analyzing amortized time taken by operations in a data structure are fully explained. The two basic techniques are the "banker's method" and the "physicist's method", and they have to do with different ways of accounting for time spent in in the different operations on a data structure so that bounds on the time spent can be computed. (The "credits" and "debits" used for accounting for time are not reflected in the code for the data structure - they are "virtual" and only used for the analysis.) Then, Okasaki adapts these methods to work for persistent data structures, and provides several fully worked out examples.

With analysis of persistent data structures explained he then goes on to describe several methods of creating efficient persistent data structures from non-persistent data structures. These methods are lazy rebuilding,use of numerical representations in building data structures, data-structural bootstrapping, and implicit recursive slowdown. These methods are all interesting, but the one which is most fun (and for me, the easiest to understand) is the use of numerical representations. In this method data structures are built by composing smaller structures in a form that is similar to the representation of a binary number--and merging and deleting items from the data structure is modeled as adding or subtracting from a number. Also, different kinds of binary number representations are used, and the use of base 3 and base 4 numbers is mentioned. [3]

The persistent data structures described are given with code (in an ML variant that includes explicit notations for lazy evaluation, and also in Haskell). After the book was published the data structures described were "productized" into a Haskell library called Edison, originally written by Chris Okasaki, but which is now maintained and enhanced by Robert Dockins and available at [...]


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1: A good starting point to look for papers on the subject of imperative persistent data structures is to search for papers co-authored by Robert E Tarjan with the word "persistent" in the title.

2: GC is part of the C++0x language and compilers will be required to support it, so that means we'll get to use it by 2015, I'm sure!

3: You can get the flavor of this kind of thing from this paper by Ralf Hinze - [...]

If you are beginning to learn functional programming, this is a good book to study. It focuses much on the "no assignment" aspect of the functional style; a good place to start. And does this on one data structure after another allowing it to be easily understood by readers with a procedural background. For the more advanced reader, the algorithmic content of the book is good reading and I find myself picking this book every year or so just to entertain myself. The applicability of the data structures is limited in that most languages come with basic libraries that suffice most of the time.

Despite the editorial description of the book, the book is really about Standard ML. It happens to have an appendix where source code has been translated -- out of order, and without reference to the text -- into Haskell. This makes it very difficult to read through the book without speaking Standard ML.

The exercises, also, are only SML. Several appear to use idiosyncratic SML features -- I say "appear" because no answers to the exercises, even the basic ones, are provided for me to check my understanding.

Essentially, the content is good, but expect to learn Standard ML to really get the most out of this book.

I'm not going to lie; I didn't make it through the second 2/3rds of this book. But that first third did more for my understanding of functional programming than the rest of my FP library did combined. This book seems to me to be the functional programming answer to Design Patterns. It's worth having on your shelf if you have any interest in functional programming, ML, or Haskell.

This book is great for someone who already understands the basics of functional programming but wants to learn more. I should note that terseness is one of this book's greatest strong points. In other words, don't assume that just because the book is short that it will be a short read. There's just a lot of information packed into so few words!

I use this book all the time to implement and improve purely functional data-structures. It is amazing the performance improvements possible by using good data-structures in functional programs.

This is the best book available on the implementation of data structures in functional programming languages (e.g. ML, Haskell). Unfortunately, much of the book covers esoteric data structures that will almost never be needed in practice. Hash tables are a major omission, likely because they don't fit well into a functional environment.