Paperback: 232 pages
Publisher: Cambridge University Press (June 13, 1999)
Product Dimensions: 6 x 0.5 x 9 inches
Shipping Weight: 14.6 ounces (View shipping rates and policies)
Average Customer Review: 4.5 out of 5 stars See all reviews (20 customer reviews)
Best Sellers Rank: #272,470 in Books (See Top 100 in Books) #30 in Books > Computers & Technology > Programming > Functional #35 in Books > Computers & Technology > Programming > Algorithms > Data Structures #49 in Books > Computers & Technology > Programming > Software Design, Testing & Engineering > Structured Design
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.
[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 . 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).