Functional Thinking

Chapter 4. Smarter, Not Harder

Switching paradigms yields benefits, allowing you to get more work done with less effort. Many functional programming constructs do just that: remove annoying implementation details for common problems.

In this chapter, I discuss two features common in functional languages: memoization and laziness.

Memoization

The word memoization was coined by Donald Michie, a British artificial-intelligence researcher, to refer to function-level caching for repeating values. Today, memoization is common in functional programming languages, either as a built-in feature or one that’s relatively easy to implement.

Memoization helps in the following scenario. Suppose you have a performance-intensive function that you must call repeatedly. A common solution is to build an internal cache. Each time you calculate the value for a certain set of parameters, you put that value in the cache, keyed to the parameter value(s). In the future, if the function is invoked with previous parameters, return the value from the cache rather than recalculate it. Function caching is a classic computer science trade-off: it uses more memory (which we frequently have in abundance) to achieve better performance over time.

Functions must be pure for the caching technique to work. A pure function is one that has no side effects: it references no other mutable class fields, doesn’t set any values other than the return value, and relies only on the parameters for input. All the methods in the java.lang.Math ...