Have you read your PFDS today?

Purely Functional Data Structures
http://www.cs.cmu.edu/~rwh/theses/okasaki.pdf

When a C programmer needs an efficient data structure for a particular problem, he or she can often simply look one up in any of a number of good textbooks or handbooks. Unfortunately, programmers in functional languages such as Standard ML or Haskell do not have this luxury. Although some data structures designed for imperative languages such as C can be quite easily adapted to a functional setting, most cannot, usually because they depend in crucial ways on assignments, which are disallowed, or at least discouraged, in functional languages. To address this imbalance, we describe several techniques for designing functional data structures, and numerous original data structures based on these techniques, including multiple variations of lists, queues, double-ended queues, and heaps, many supporting more exotic features such as random access or efficient catenation.

In addition, we expose the fundamental role of lazy evaluation in amortized functional data structures. Traditional methods of amortization break down when old versions of a data structure, not just the most recent, are available for further processing. This property is known as persistence, and is taken for granted in functional languages. On the surface, persistence and amortization appear to be incompatible, but we show how lazy evaluation can be used to resolve this conflict, yielding amortized data structures that are efficient even when used persistently. Turning this relationship between lazy evaluation and amortization around, the notion of amortization also provides the first practical techniques for analyzing the time requirements of non-trivial lazy programs.
 
Finally, our data structures offer numerous hints to programming language designers, illustrating the utility of combining strict and lazy evaluation in a single language, and providing non-trivial examples using polymorphic recursion and higher-order, recursive modules.

>>17,19

the programmer hopes the compiler is clever enough to optimize away the overhead, and at the end of the day it turns out it isn't.

You can get mad dosh for making problems from thin air. Read Machiavelli some time.

Look at all the cash generated by null-terminated strings. They're slow, hard to use safely, need escaping when including binary data, make algorithms that need a length require an extra scan, and scanning for the length is not only O(n), but thrashes your cache. People get grants and sell hardware for making it faster to scan for a 0 instead of, you know, including the length.