# State Monad

# Numbering the nodes of a tree with a counter

We have a tree data type like this:

data Tree a = Tree a [Tree a] deriving Show

And we wish to write a function that assigns a number to each node of the tree, from an incrementing counter:

tag :: Tree a -> Tree (a, Int)

# The long way

First we'll do it the long way around, since it illustrates the State monad's low-level mechanics quite nicely.

import Control.Monad.State

-- Function that numbers the nodes of a `Tree`.
tag :: Tree a -> Tree (a, Int)
tag tree = 
    -- tagStep is where the action happens.  This just gets the ball
    -- rolling, with `0` as the initial counter value.
    evalState (tagStep tree) 0

-- This is one monadic "step" of the calculation.  It assumes that
-- it has access to the current counter value implicitly.
tagStep :: Tree a -> State Int (Tree (a, Int))
tagStep (Tree a subtrees) = do
    -- The `get :: State s s` action accesses the implicit state
    -- parameter of the State monad.  Here we bind that value to
    -- the variable `counter`.
    counter <- get 

    -- The `put :: s -> State s ()` sets the implicit state parameter
    -- of the `State` monad.  The next `get` that we execute will see
    -- the value of `counter + 1` (assuming no other puts in between).
    put (counter + 1)

    -- Recurse into the subtrees.  `mapM` is a utility function
    -- for executing a monadic actions (like `tagStep`) on a list of
    -- elements, and producing the list of results.  Each execution of 
    -- `tagStep` will be executed with the counter value that resulted
    -- from the previous list element's execution.
    subtrees' <- mapM tagStep subtrees  

    return $ Tree (a, counter) subtrees'

# Refactoring

# Split out the counter into a postIncrement action

The bit where we are getting the current counter and then putting counter + 1 can be split off into a postIncrement action, similar to what many C-style languages provide:

postIncrement :: Enum s => State s s
postIncrement = do
    result <- get
    modify succ
    return result

# Split out the tree walk into a higher-order function

The tree walk logic can be split out into its own function, like this:

mapTreeM :: Monad m => (a -> m b) -> Tree a -> m (Tree b)
mapTreeM action (Tree a subtrees) = do
    a' <- action a
    subtrees' <- mapM (mapTreeM action) subtrees
    return $ Tree a' subtrees'

With this and the postIncrement function we can rewrite tagStep:

tagStep :: Tree a -> State Int (Tree (a, Int))
tagStep = mapTreeM step
    where step :: a -> State Int (a, Int)
          step a = do 
              counter <- postIncrement
              return (a, counter)

# Use the Traversable class

The mapTreeM solution above can be easily rewritten into an instance of the Traversable class:

instance Traversable Tree where
    traverse action (Tree a subtrees) = 
        Tree <$> action a <*> traverse action subtrees

Note that this required us to use Applicative (the <*> operator) instead of Monad.

With that, now we can write tag like a pro:

tag :: Traversable t => t a -> t (a, Int)
tag init t = evalState (traverse step t) 0
    where step a = do tag <- postIncrement
                      return (a, tag)

Note that this works for any Traversable type, not just our Tree type!

# Getting rid of the Traversable boilerplate

GHC has a DeriveTraversable extension that eliminates the need for writing the instance above:

{-# LANGUAGE DeriveFunctor, DeriveFoldable, DeriveTraversable #-}

data Tree a = Tree a [Tree a]
            deriving (Show, Functor, Foldable, Traversable)

# Remarks

Newcomers to Haskell often shy away from the State monad and treat it like a taboo—like the claimed benefit of functional programming is the avoidance of state, so don't you lose that when you use State? A more nuanced view is that:

  • State can be useful in small, controlled doses;
  • The State type provides the ability to control the dose very precisely.

The reasons being that if you have action :: State s a, this tells you that:

  • action is special because it depends on a state;
  • The state has type s, so action cannot be influenced by any old value in your program—only an s or some value reachable from some s;
  • The runState :: State s a -> s -> (a, s) puts a "barrier" around the stateful action, so that its effectfulness cannot be observed from outside that barrier.

So this is a good set of criteria for whether to use State in particular scenario. You want to see that your code is minimizing the scope of the state, both by choosing a narrow type for s and by putting runState as close to "the bottom" as possible, (so that your actions can be influenced by as few thing as possible.