These Are Not The JavaScripts You Are Looking For

Let Gone Wild

Clojure gives me the tools to write in a functional style, but I've found myself unconsciously using let to write imperatively. Why? Probably because it is so easy:

( defn postage-cost [ dimensions weight shipping-days ] ( let [[ height width ] dimensions big? ( and ( < height 10 ) ( < width 10 )) express? ( > 2 shipping-days ) multiple ( cond ( and express? big? ) 5 express? 3 big? 2 :else 1 )] ( * multiple weight )))

My imperative solution comes from an imperative problem-solving mindset:

Grab the height and width from the dimensions. Check height and width to see if the package is big. Check the shipment days to see if we need express shipment. Based on the former calculations, determine the multiplier. Multiply the weight by the multiplier.

There's nothing especially wrong with such logical step-by-step thinking, but I've written a function that is useless except for a very narrow range of situations. When you tell a computer exactly what to do and exactly how to do it, you end up with a brittle program.

A Functional Solution

So how should I approach this according to Functional Programming principles? What are those principles? There's no definitive list, but I like these:

Immutable data

Bottom-up functions

Data

First, put your configuration data where it belongs:

( def config { :pricing { :express 3 :big 2 :regular 1 } :sizing { :big-height 10 :big-width 10 } :speed { :express 2 }})

More Data

"Garbage in, garbage out"

Every program's job is to transform data (input) into data (output). While thinking imperatively leads me to first seek solutions, thinking functionally leads me to first seek data:

Write down the raw data you expect as input. Put away the urge to solve the problem. Write down the raw data you expect as output.

;;; Input { :width 10 :height 20 :weight 4 :days-to-ship 2 } ;;; Some functions do their thing ;;; Output { :width 10 :height 20 :weight 4 :days-to-ship 2 :express? true :big? true :multiplier 5 :price 20 }

Transforming Data

Now that we have the start and end of our journey through data, we can begin to think of solutions. But these solutions have a sharp focus: transforming data from the bottom-up.

Identify the next part of the data to update. Determine how to calculate the new data. Return a slighly updated version of the old data.

Add :price

( defn add-price [{ :keys [ weight multiplier ] :as package }] ;; destructuring ( ->> ( * weight multiplier ) ( assoc package :price ))) ;; returns a copy of `package`

Add :multiplier

( defn add-multiplier [{ :keys [ express big regular ]} { :keys [ express? big? ] :as package }] ( ->> ( cond ( and express? big? ) ( + express big ) express? express big? big :else regular ) ( assoc package :multiplier )))

Add :big?

( defn add-big [{ :keys [ big-height big-width ]} { :keys [ height width ] :as package }] ( ->> ( and ( < big-height height ) ( < big-width width )) ( assoc package :big? )))

Add :express?

( defn add-express [{ :keys [ express ]} { :keys [ days-to-ship ] :as package }] ( ->> ( < days-to-ship express ) ( assoc package :express? )))

Combine functions

( defn add-shipping [{ :keys [ pricing sizing speed ] :as config } package ] ( ->> package ( add-big sizing ) ( add-express speed ) ( add-multiplier pricing ) add-price ))

Our final result couldn't be more clear. Here's where I jump for joy and declare my love for Functional Programming!

Value and Cost

"Move fast and break things"

The FP code is five times longer than the imperative solution. That's a huge cost. What kind of code could warrant that kind of effort? I'd propose the following:

Readable: because small functions can be understood Reusable: because discrete functions can be reused Extensible: because pure functions can be composed