The Functional Programming paradigm encourages you to think of your application with respect to data, and the functions that operate on them. When explaining this to people unfamiliar with the concept, I like to reference my favorite recipe for Egg Salad.
Let’s say you start with a box of a dozen eggs. In order to make an egg salad, one would follow these steps
In functional programming parlance, you have just taken a list of eggs, and applied the following functions: filter, map, mapcat, and reduce.
Let’s look at each of these functions in detail.
The first step of our algorithm is to visually inspect each egg, and filter out each rotten egg.
Coded in the above statement, are the core principles behind a filter
The next step of our algorithm iterates over every egg that remained from the filter, boiling each one.
Chopping one egg will give you a list of egg pieces. Mapping over a list of eggs and chopping each one, will give you a list of list of egg pieces. Concatenating those list gives you a list of egg pieces.
The final step in the recipe is to combine all the egg pieces into a bowl. Unlike the other steps, this step does not output a list of items, it outputs a single bowl of egg salad.
The above algorithm transforms eggs into an egg salad. But an important question remains. How many eggs do I need for my egg salad? Let’s say I start with 12 eggs (10 good ones and 2 rotten ones), and I need 5 eggs worth of pieces for my egg salad.
The first approach we can take is the non lazy approach. This is what is taken by Ruby, Python and most other languages by default (Ruby since implemented lazy collections, but the default enum functions are eager by default).
Let us consider the following ruby pseudo-code: eggs.filter(&:not_rotten?).map(:boil).flat_map(&:peel_and_chop).reduce(into_bowl)
Assuming we filter out the two bad eggs, we will end up boiling, peeling and chopping 10 good egg pieces. When reducing, we only need 5 eggs worth of pieces for our egg salad, which means that 5 eggs are wasted. Worse, they’ve been chopped up, so I can’t put them back into the refrigerator.
Is there a way that we can consume an egg only when needed?
Let’s now take an extremely lazy approach. filter, map, mapcat and take can all return a lazy collection, meaning that items are realised as needed. Only reduce will force the lazy sequences. This means that as reduce needs more pieces to fill it’s bowl, a single egg will be inspected, boiled, peeled and chopped.
This guarantees that less than one egg is wasted, as a single egg is processed at a time.
However, boiling each egg individually is going to be tedious and time consuming. Is there a way to club eggs so that they can all get boiled together?
Clojure takes a slightly hybrid approach. Instead of boiling one egg at a time, which is time consuming, Clojure will chunk items together, and boil a batch of 32 eggs at a time. This may not give a lot of benefits when you need only 5 eggs, but it ensures that you will waste a maximum of 31 eggs, even if you are making 100 bowls of egg salad from a pool of 600 eggs.
In the case of Clojure, these optimisations happen under the hood, and you do not have easy control over these. A simple technique to emulate the same thing in any language is to chunk a list into smaller lists-of-5-items, and mapcat each list-of-5-items into the final result.
Once you start dealing with lazy sequences, it is possible to imagine infinitely long sequences. For example, you may tie the source of your eggs to your amazon account, so that every time you are out of eggs, it will automatically order a new batch of dozen. Infinite sequences know that you will never need to realise the entire sequence, you will probably consume some finite amount of items and ignore the rest.
Further, infinite sequences get items just in time. If you only order 12 eggs at a time from Amazon as you need it, you only need storage for 12 eggs at a time. All other eggs are either unrealised (in Amazon), or processed (in the bowl). Storage here is a metaphor for memory required.
Haskell takes this to an extreme level. I have no idea how this works, but Haskell will not start to boil the first egg until somebody tries to eat the egg salad. It is widely believed that when Simon Peyton Jones, the lead developer of the Glasgow Haskell Compiler, orders a pizza, a new pizza restaurant gets a license.
To Quote Carl Sagan:
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