“In The Mythical Man-Month, Frederick Brooks proposed that the productivity of a group of programmers does not grow linearly with its size. As the size of the group increases, the productivity of individual programmers goes down. The experience of Lisp programming suggests a more cheerful way to phrase this law: as the size of the group decreases, the productivity of individual programmers goes up. A small group wins, relatively speaking, simply because it’s smaller.” (On Lisp, p 5.)

“Sophisticated users now demand so much from software that we can’t possibly anticipate all their needs. They themselves can’t anticipate all their needs. But if we can’t give them software which does everything they want right out of the box, we can give them software which is extensible. We transform our software from a mere program into a programming language, and advanced users can build upon it the extra features that they need.” (On Lisp, p 5.)

“So yes, reading a bottom-up program requires one to understand all the new operators defined by the author. But this will nearly always be less work than having to understand all the code that would have been required without them.” (On Lisp, p 59.)

I imagine that most people picking up On Lisp these days are mainly interested in two things: how do you use macros and what can lisp do that other languages can’t do? But after an inspiring introduction that deftly weaves in themes that are regularly discussed and debated even fifteen years after the book’s publication, the reader is faced with several dry chapters that focus on functions and utilities. To someone with little experience with functional programming, basic computer science, or extensive lisp coding, much of it will seem pointlessly tedious and pedantic.

I was just such a person last May just before starting this blog. I really wanted to skip ahead to the good stuff and savor Graham’s prose, but I was like Luke Skywalker cutting out on his training to go fight Darth Vader. (I was just about to get my face smashed by gigantic futuristic heavy things as I tried to wrap my mind around a lot of mind-blowing material. Or something like that.) I strained myself through several chapters of On Lisp even though I understood little of it and finally exhausted myself. And yet, I have code that I wrote about that time that violates the spirit of everything Graham was trying to say! I was completely lost.

There’s really only a few core ideas that you need to know about, though. You need to know what functions can do inside and out before you can understand when to use macros. You need to know about recursion, iteration, accumulators, and mapping. You need to be able to reflexively see the skeleton of a function, know how to abstract it out, and know how create customizable functions that take functions for arguments. And you need to have coded enough of this stuff in Lisp such that you’ve done certain things over and over and over again. If you get to chapter 4 and don’t immediately think, “man, these utility functions are all stuff I wish was already built into the language!” then you probably haven’t done enough Lisp coding to appreciate the book. SICP covers all of these things and more. If you can work through the first two chapters of SICP, then you’ll be more than prepared for On Lisp.

Reading On Lisp now after working through parts of SICP and doing a few small practice projects, I can finally understand what it’s saying. It’s really satisfying to finally “get” what Paul says about closures. Here’s a brief summary of the points about closures he covers:

* Closures emerge from lexical scoping. (Lexical scoping forces the system to save copies of variables when functions are defined and then associate those copies with the functions.)

* Example 1: Create a function that uses a lambda function that references one of the original arguments. (Make an implicit “function factory”.)

* Example 2: Define two functions that share the same counter variable. (Eliminate the need for messy global variables.)

* Example 3: Create a function that returns a customized function based on the arguments. (Make an explicit “function factory”.)

* Example 4: Alter example three so that the customized functions can change their state after they’ve been created. (Use optional arguments on a closure to allow the users to change it’s behavior later– sort of like properties on an object.)

* Example 5: Create a function that returns a group of closures that all use the same data objects.

* Example 6 (Chapter 5): Create a function that takes a function as its argument and returns a function for its value. (In the examples, we get one for Complement and another for Composition. These functions are mainly used to simplify and refactor the core Common Lisp library. SICP‘s coverage of Newton’s Method is much more exciting and as a bonus, it will help you understand the famous fast inverse sqrt routine.)

* Example 7 (Chapter 6): Elegance and speed with closures: Instead of defining a data structure and then defining a set of functions to access it, you can define a “smart” data structure by using closures that keep their own state and reference other closures. The line between code and data can be further blurred here by writing routines to compile your closure-based data structures. “In Lisp we can sometimes use closures as a representation. Within a closure, variable bindings can store information, and can also play the role that pointers play in constructing complex data structures. By making a group of closures which share bindings, or can refer to one another, we can create hybrid objects which combine the advantages of data structures and programs.” (On Lisp, p 76.)

Looking at these examples together, it becomes clear that a typical OOP style emphasizes a top-down approach, while closures encourage a more finely grained bottom-up style. Keep in mind that the examples of closures here are not the final iterations that Graham wants you to take away from the book. In later sections he will improve the code using macros to increase the efficiency and generality of the samples.

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