I’m often surprised by the fact that many people see monads as the be all and end all of Haskell’s abstraction techniques. Beginners often struggle over them thinking that they’re something incredibly important and complex. In reality though, monads are neither important, nor complex, and that’s what I aim to show with this little tutorial about what other abstraction techniques you can use, and when they’re appropriate.

## First things first – IO

The reason a lot of people come across monads is that they want to get on with interacting with the outside world – they want to do I/O. I’m going to get this out of the way quickly. In Haskell, IO is kept in a carefully constrained box, because unconstrained it violates referential transparency (that is, you can get multiple answers from the same call to the same function depending on what the user happens to type/how they wibble the mouse/etc). You can recognise functions in this box by their type signature – they involve a type that looks like this: `IO something`

.

Here’s a few examples:

-- Call this, and you get back a Char,
-- carefully wrapped up in an IO type.
getChar :: IO Char
-- This one doesn't have an interesting result,
-- only a unit value wrapped up in an IO type.
putStrLn :: String -> IO ()

These can be stuck together with the handy do notation:

main = do
putStrLn "What is your name?"
name <- getLine
putStrLn ("Your name is " ++ name)

There, that’s that out of the way – we can do IO now, and we didn’t need to understand anything nasty or complex.

## Some Abstraction techniques – Dealing with values in boxes

We often deal with values that are hidden inside other types. For example, we use lists, to hide values in, we use Maybes etc. What would be nice, is if we could apply a function to the values hiding in those boxes. Early in learning Haskell I’m sure you will have met a function that can do this for lists: map. This can be generalised though: Enter the *Functor*. Functors can do one thing, and one thing only, they can apply a function to a value inside an outer construction. They do this with the function fmap (or Functor map). Lets look at some examples:

For Lists, fmap is simply map:

> fmap (+1) [1,2,3,4]
[2,3,4,5]

For Maybe values fmap lets us apply a function to the value inside a just:

> fmap (+1) (Just 1)
Just 2
> fmap (+1) Nothing
Nothing

For Tuples, fmap lets us apply a function to the second half of the tuple (if we import an extra module that defines it):

> import Control.Applicative
> fmap (+1) (1,2)
(1,3)

We can use fmap to target a function into several layers of boxes by composing applications:

> (fmap . fmap) (+1) [(1,2), (3,4)]
[(1,3),(3,5)]

Here, the first fmap pushes (fmap (+1)) inside the list, and the second fmap pushes (+1) inside the tuples.

## Putting things in boxes

All that isn’t very useful if we can’t actually put something in a box in the first place. This is where the pure function comes in handy. This function lets us wrap anything we like in a box.

> pure 1 :: [Int]
[1]
> pure 1 :: Maybe Int
Just 1
> pure 1 :: Either String Int
Right 1

In Haskell, the pure function is in the Applicative class (you’ll need to import Control.Applicative). The Applicative class does some other interesting things as we’ll see in a minute. Because of this it would be nice if pure were separated into its own little class all on its own, but unfortunately that isn’t the way it is in Haskell (at the moment at least).

## Boxes everywhere

So, we’ve seen how to put something in a box, and we’ve seen how to apply a function to a value in a box, but what if our function is in a box too? At this point, the Applicative class really comes into its own. The (<*>) function from Applicative lets us apply boxes to each other as long as they have the right types of values inside.

> (Just (+1)) <*> (Just 1)
Just 2
> [(+1), (*2), (^3)] <*> [1,2,3]
[2,3,4,2,4,6,1,8,27]

That second result is not entirely clear – what’s going on? Well, the (<*>) function has applied each function to each argument in turn, and bundled up all the results in one list. (+1) gets applied to each argument, generating the results 2,3 and 4; (*2) gets applied to each argument, generating the results 2,4 and 6; and finally (^3) gets applied to each argument, generating the results 1,8 and 27.

An important note: All Applicatives are also Functors. You can implement fmap for any Applicative like this:

fmap f a = (pure f) <*> a

Applicative in fact does this for you, but calls the function (<$>).

## Functions that produce boxes

When we have functions that produce values that are hidden inside boxes, we have a problem. Each time we apply the function we get an additional layer of boxes, this isn’t particularly pretty, nor composable. This is where monads come in. Monads add a single function called join, which is used to flatten out the layers of boxes:

> join (Just (Just 2))
Just 2
> join [[1],[2],[3,4,5]]
[1,2,3,4,5]

The join function lets us compose our box-producing functions more easily. We now fmap our box producing function over values in a box. This results in a 2-layer set of boxes. We can then use join to squash that back down again. This pattern is so useful that we call it “bind” or (=<<).

Some people like to define this the other way round:

This allows a very imperative style of programming where we ask the language to take the result of one computation, push it through a function, take the results, push them through another function, etc.

As with Applicatives and Functors, all Monads are Applicatives. We can define the (<*>) function using only bits of a Monad and the pure function:

f <*> a = f >>= (\fv -> a >>= (pure . fv))

We can see here a common pattern with monadic programming. We bind a function returning a monadic value into a lambda. Haskell provides a syntactic sugar for doing this called do notation:

f <*> a = do
fv <- f
av <- a
pure (fv av)

We can now see clearly that IO in Haskell is not using any magic at all to introduce an imperative concept to a functional language, instead the IO type is simply a monad. Remember, this means that it’s a functor and an applicative too, so we can use <$> and <*> wherever we please in IO code to apply functions to IO values.

## Why you don’t always want to go for Monads

As we’ve seen, Monad sits atop a set of classes proudly the most powerful of all, but that doesn’t mean we want to use it all the time. As we’ve seen, Monad gives us a very imperative feel to our code – it reveals an order that isn’t necessarily there. Do notation particularly seems to suggest (in our example above) that we should first take the value out of f, then take the value out of a, and then apply the two. In reality, this order is not there, the Haskell runtime is free to evaluate these in any order it likes. This can make such language constructs dangerous. Firstly, we’re functional programmers because we like describing what things “are”, not what steps you should take to produce them. Secondly, the steps we seem to give here, are not the ones that the run time will really take in the end.

Lets look at an example of when we really shouldn’t use Monads. We have excellent Parser combinators in the Parsec library. These let us define small parsers, and stick them together using the Monadic interface. Lets define a small parser to parse an Int that may or may not be in a string:

parseInt = do
ds <- many1 digit
pure $ read ds
parseMaybe p =
(do n <- p
pure (Just n))
<|> (pure Nothing)

We are expressing an ordering that we don’t intend to – first we accept at least one digit into ds, then we read them and rewrap them in a parser. In parseMaybe first we parse something, and take the value out into n, then we wrap it in Just, and give it back.

This isn’t clear. Why couldn’t we just describe the grammar? Why do we have to specify an order? Lets patch parsec to provide an Applicative instance:

instance Applicative (GenParser a st) where
(<*>) = ap
pure = return

Note that I’m using a shorter version of the definition of (<*>) in a monad using the ap function. Now we may use the applicative functor interface:

parseInt = read <$> many1 digit
parseMaybe p = (Just <$> p) <|> (pure Nothing)

Not only are these definitions shorter, but we can quickly and easily see their meanings – an integer is many digits, with read applied to get them into a form we can use in Haskell.

Lets look at a more complex example:

data Record = R { firstCol, secondCol :: Maybe Int }
deriving (Show)
parseRecord = do
col1 <- parseMaybe parseInt
char ','
col2 <- parseMaybe parseInt
char '\n'
pure (R col1 col2)

Again, we’re specifying an order we don’t want to see. Lets look at this in applicative style:

parseRecord =
R <$> (parseMaybe parseInt <* char ',')
<*> (parseMaybe parseInt <* char '\n')

Note the use of the (<*) function – this simply takes the value from the left hand parser, and passes it up, ignoring the value returned by the right hand parser. We can now see that parseRecord constructs a record from two maybe Ints, separated with a comma. We haven’t introduced any orderings that we don’t need to, and we’ve even condensed our code a little.

## Conclusions

We’ve seen the hierarchy of classes in Haskell in all its glory, rather than focusing unduly on the Monad, we’ve seen that the Monad interface, while powerful, is not always desirable. Hopefully we’ve seen that a lot of our monadic code can be cleaned up to use the Applicative (or maybe even Functor) interface instead.