Today we look at a single module that clocks in at a mere 200 lines of code, including comments. In these few lines though is an optimisation that has a wide range of use cases - an optimisation taking an operation from O(n) to O(1). Now that’s my kinda optimisation!

The `dlist`

library provides an API that allows list appending to run in O(1) rather than O(n) time. Before we look at the magic that makes this unbelievable feat possible, let’s take a quick look at how the API works.

```
> toList $ append (fromList [1..5]) (fromList [1..10])
[1,2,3,4,5,1,2,3,4,5,6,7,8,9,10]
```

We use `fromList`

to convert a Haskell list into a dlist. Next, we use the `dlist`

specific `append`

to combine 2 lists. Note that `dlist`

is a monoid so we could also use the more general `<>`

operator. Finally, to get back out of `dlist`

by calling `toList`

. There’s not much more to it!

So, now that we’ve seen this at work, we can ask ourselves how this works. The key to `dlist`

lies in function composition and partially applied functions. Rather than storing complete lists, and building new lists every time we call `append`

, we instead store a *function* that given a list will the original list prepended to the input to the function. This now lets us use function composition to build up our final list, and function composition is O(1). Let’s see this is a bit of code:

```
> :t ([1, 2] ++)
([1, 2] ++) :: Num a => [a] -> [a]
> :t ([1, 2] ++) . ([3, 4] ++)
([1, 2] ++) . ([3, 4] ++) :: Num a => [a] -> [a]
> ([1, 2] ++) . ([3, 4] ++) $ []
[1,2,3,4]
```

That’s almost all there is to it. Dlist simply wraps this up in a new type and provides instances for `Monad`

, `Monoid`

, and a few other type classes we’d expect. It should that `dlist`

doesn’t make *everything* O(1) - at some point you will have to construct the list, which will be at least O(n). However, for specific usage patterns `dlist`

can give you a good boost in performance.

You can contact me via email at ollie@ocharles.org.uk or tweet to me @acid2. I share almost all of my work at GitHub. This post is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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