Purely Functional Data StructuresMost books on data structures assume an imperative language such as C or C++. However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. The author includes both classical data structures, such as redblack trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for selfstudy. 
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This is a great book. An improved version of Okasaki's PhD thesis, the book introduces the reader to the notion of efficient data structures which still get to be purely functional (i.e. not imperative, no sideeffects).
A common problem with purely functional data structures is that, since there is no state, we can't change the value of a variable once it has been assigned. We can always create a new variable with the same exact name, but it will be a completely new variable and any pointer to the previous variable would be lost. This is called Persistency. Persistency makes building efficient purely functional data structures and algorithms, even for basic tree searching and sorting, an intricate craft.
In this book, we are presented with an arsenal of techniques to build purely functional data structures which are efficient in the sense that they compete well with imperative counterparts. The book uses lazy evaluation and memoization as the keys to unlock efficiency. The algorithms are presented as working code in Standard ML (and in Haskell in the appendix) which is great. Everything is very well explained and rigorous. Techniques for computing the complexity of algorithms are already presented (banker's method and physicist method).
Make no mistake. This is not an introduction to functional languages. Knowledge of those languages is assumed and, although an experienced programmer in other kind of languages may profit from it without knowing any functional languages, if you are looking for an introduction, buy something else.
Having said that, if you are familiar with Haskell or ML (any dialect), this is book is a great way to both (1) learn important techniques that you can apply when building your own data structures and algorithms and (2) as a reference for basic functional data structures and algorithms (ordered sets, redblack trees, etc).
I did buy this book and I am very pleased with it.
Contents
Introduction  1 
12 Strict vs Lazy Evaluation  2 
13 Terminology  3 
14 Approach  4 
Persistence  7 
22 Binary Search Trees  11 
23 Chapter Notes  15 
Some Familiar Data Structures in a Functional Setting  17 
71 Scheduling  84 
72 RealTime Queues  86 
73 Binomial Heaps  89 
74 BottomUp Mergesort with Sharing  94 
75 Chapter Notes  97 
Lazy Rebuilding  99 
82 Global Rebuilding  101 
83 Lazy Rebuilding  104 
32 Binomial Heaps  20 
33 RedBlack Trees  24 
34 Chapter Notes  29 
Lazy Evaluation  31 
42 Streams  34 
43 Chapter Notes  37 
Fundamentals of Amortization  39 
52 Queues  42 
53 Binomial Heaps  45 
54 Splay Heaps  46 
55 Pairing Heaps  52 
56 The Bad News  54 
57 Chapter Notes  55 
Amortization and Persistence via Lazy Evaluation  57 
62 Reconciling Amortization and Persistence  58 
63 The Bankers Method  61 
64 The Physicists Method  68 
65 Lazy Pairing Heaps  79 
66 Chapter Notes  81 
Eliminating Amortization  83 
84 DoubleEnded Queues  106 
85 Chapter Notes  113 
Numerical Representations  115 
91 Positional Number Systems  116 
93 Skew Binary Numbers  130 
94 Trinary and Quaternary Numbers  138 
95 Chapter Notes  140 
DataStructural Bootstrapping  141 
101 Structural Decomposition  142 
102 Structural Abstraction  151 
103 Bootstrapping To Aggregate Types  163 
104 Chapter Notes  169 
Implicit Recursive Slowdown  171 
112 Catenable DoubleEnded Queues  175 
113 Chapter Notes  184 
Haskell Source Code  185 
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