Today we're going to take a peek at the Update monad! It's a monad which was formalized and described in Update Monads: Cointerpreting Directed Containers by Danel Ahman and Tarmo Uustalu. Most folks probably haven't heard of it before, likely because most of what you'd use it for is well encompassed by the Reader, Writer, and State monads. The Update Monad can do everything that Reader, Writer, and State can do, but as a trade-off tends to be less efficient at each of those tasks. It's definitely still worth checking out though; not only is it interesting, there are a few things it handles quite elegantly that might be a bit awkward to do in other ways.

Heads up; this probably isn't a great post for absolute beginners, you'll want to have a decent understanding of monoids and how StateT works before you dive in here.

For readers who've spent a bit of time in Javascript land you may notice that the Update Monad is basically a formalization of the Flux architecture, most commonly associated with the Redux library; although of course the Update Monad paper came first 😉. Most of the concepts carry over in some form. The Store in redux corresponds to the state of the Update monad, the Action s in Redux correspond directly to our monoidal Actions in the Update monad, and the view and dispatcher are left up to the implementor, but could be likened to a base monad in a monad transformer stack which could render, react, or get user input (e.g. IO).

The Update monad is very similar to the State monad; and in fact you can implement either of them in terms of the other! Each has tasks at which it excels; the Update Monad is good at keeping an audit log of updates and limiting computations to a fixed set of permissible updates. State on the other hand has a simpler interface, less boiler-plate, and is MUCH more efficient at most practical tasks. It's no wonder that State won out in the end, but the Update monad is still fun to look at!

The Update Monad kinda looks like Reader, Writer and State got into a horrific car accident and are now hopelessly entangled! Each computation receives the current computation state (like Reader ) and can result in a monoidal action (like Writer ). The action is them applied to the state according to a helper typeclass which I'll call ApplyAction : it has a single method applyAction :: p -> s -> s ; which applies a given monoidal action p to a state resulting in a new state. This edited state is passed on to the next computation (like State ) and away we go! Here's my implementation of this idea for a new data type called Update .

class ( Monoid p) => ApplyAction p s where p)p s applyAction :: p -> s -> s data Update s p a = Update s p a { runUpdate :: (s -> (p, a)) (s(p, a)) } deriving ( Functor ) instance ( ApplyAction p s) => Applicative ( Update s p) where p s)s p) pure a = Update $ \_ -> ( mempty , a) \_, a) Update u <*> Update t = Update $ \s \s -- Run the first 'Update' with the initial state -- and get the monoidal action and the function out -> let (p, f) = u s (p, f)u s -- Run the second 'Update' with a state which has been altered by -- the first action to get the 'a' and another action = t (applyAction p s) (p', a)t (applyAction p s) -- Combine the actions together and run the function in (p' <> p, f a) (p'p, f a) instance ( ApplyAction p s) => Monad ( Update s p) where p s)s p) Update u >>= f = Update $ \s \s -- Run the first 'Update' with the initial state -- and get the monoidal action and the function out -> let (p, a) = u s (p, a)u s -- Run the given function over our resulting value to get our next Update Update t = f a f a -- Run our new 'Update' over the altered state = t (applyAction p s) (p', a')t (applyAction p s) -- Combine the actions together and return the result in (p <> p', a') (pp', a')

We could of course also implement an UpdateT monad transformer, but for the purposes of clarity I find it's easier to understand the concrete Update type. If you like you can take a peek at some other fun implementations here. Hopefully it's relatively clear from the implementation how things fit together. Hopefully you can kind of see the similarities to Reader and Writer; we are always returning and combining our monoidal actions as we continue along, and each action has access to the state, but can't directly modify it (you may only modify it by providing actions). It's also worth noting that within any individual step has only the latest state and it's not possible to view any previous actions which may have occurred, just like the Writer monad.

Now that we've implemented our Update Monad we've got our >>= and return ; but how do we actually accomplish anything with it? There's no MonadUpdate type-class provided in the paper, but here's my personal take on how to get some utility out of it, I've narrowed it down to these two methods which seem to encompass the core ideas behind the Update Monad:

{-# LANGUAGE FunctionalDependencies #-} class ( ApplyAction s p, Monad m) => s p,m) -- Because each of our methods only uses p OR m but not both -- we use functional dependencies to assert to the type system that -- both s and p are determined by 'm'; this helps GHC be confident -- that we can't end up in spots where types could be ambiguous. MonadUpdate m s p | m -> s , m -> p m s ps , m where putAction :: p -> m () m () getState :: m s m s

You'll notice some similarities here too! putAction matches the signature for tell , and getState matches ask ! This class still provides new value though, because unlike Reader and Writer the environment and the actions are related to each other through the ApplyAction class; and unlike get and put from State our putAction and getState operate over different types; you can only put actions, and you can only get state. We can formalize the expected relationship between these methods with these laws I made up (take with a deluge of salt):

-- Applying the 'empty' action to your state shouldn't change your state mempty == id applyAction -- Putting an action and then another action should be the same as -- putting the combination of the two actions. -- This law effectively enforces that `bind` is -- employing your monoid as expected >> putAction q == putAction (p `mappend` q) putAction pputAction qputAction (pq) -- We expect that when we 'put' an action that it gets applied to the state -- and that the change is visible immediately -- This law enforces that your implementation of bind -- is actually applying your monoid to the state using ApplyAction <$> getState == putAction p >> getState applyAction pgetStateputAction pgetState

Okay! Now of course we have to implement MonadUpdate for our Update monad; easy-peasy:

instance ( ApplyAction p s) => MonadUpdate ( Update p s) p s where p s)p s) p s = Update $ \_ -> (p, ()) putAction p\_(p, ()) = Update $ \s -> ( mempty , s) getState\s, s)

All the plumbing is set up! Let's start looking into some actual use-cases! I'll start by fully describing one particular use-case so we get an understanding of how this all works, then we'll experiment by tweaking our monoid or our applyAction function.

A Concrete Use-Case

Let's pick a use-case which I often see used for demonstrating the State monad so we can see how our Update monad is similar, but slightly different!

We're going to build a system which allows users to interact with their bank account! We'll have three actions they can perform: Deposit , Withdraw , and CollectInterest . These actions will be applied to a simple state BankAccount Int which keeps track of how many dollars we have in the account!

Let's whip up the data types and operations we'll need:

-- Simple type to keep track our bank balance newtype BankBalance = BankBalance Int deriving ( Eq , Ord , Show ) -- The three types of actions we can take on our account data AccountAction = Deposit Int | Withdraw Int | ApplyInterest deriving ( Eq , Ord , Show ) -- We can apply any of our actions to our bank balance to get a new balance processTransaction :: AccountAction -> BankBalance -> BankBalance Deposit n) ( BankBalance b) processTransaction (n) (b) = BankBalance (b + n) (bn) Withdraw n) ( BankBalance b) processTransaction (n) (b) = BankBalance (b - n) (bn) -- This is a gross oversimplification... -- I really hope my bank does something smarter than this -- We (kinda sorta) add 10% interest, truncating any cents. -- Who likes pocket-change anyways ¯\_(ツ)_/¯ ApplyInterest ( BankBalance b) processTransactionb) = BankBalance ( fromIntegral balance * 1.1 ) balance

Now we've got our Action type and our State type, let's relate them together using ApplyAction .

instance ApplyAction AccountAction BankBalance where = processTransaction applyActionprocessTransaction

One problem though! AccountAction isn't a monoid! Hrmmm, this is a bit upsetting; it seems to quite clearly represent the domain we want to work with, I'd really rather not muck up our data-type just to make it fit here. Maybe there's something else we can do! In our case, what does it mean to combine two actions? For a bank balance we probably just want to run the first action, then the second one! We'll need a value that acts as an 'empty' value for our monoid's mempty too; for that we can just have some notion of performing no actions!

There are a few ways to promote our AccountAction type into a monoid with these properties; but one in particular stands out (I can already hear some of you shouting it at your screens). That's right! The Free Monoid A.K.A. the List Monoid! Lists are kind of a special monoid in that they can turn ANY type into a monoid for free! We get mappend == (++) and mempty == [] . This means that instead of actually combining things we kinda just collect them all, but fear not it still satisfies all the monoid laws correctly. This isn't a post on Free Monoids though, so we'll upgrade our AccountAction to [AccountAction] and move on:

instance ApplyAction [ AccountAction ] BankBalance where = applyAction actions balance let allTransactions :: BankBalance -> BankBalance = appEndo $ foldMap ( Endo . processTransaction) ( reverse actions) allTransactionsappEndoprocessTransaction) (actions) in allTransactions balance allTransactions balance

We can keep our processTransaction function and partially apply it to our list of Actions giving us a list of [BankBalance -> BankBalance] ; we can then use the Endo monoid to compose all of the functions together! Unfortunately Endo does right-to-left composition, so we'll need to reverse the list first (keeners will note we could use Dual . Endo for the same results). Then we use appEndo to unpack the resulting BankBalance -> BankBalance which we can apply to our balance! Now that we have an instance for ApplyAction we can start writing programs using Update .

useATM :: Update [ AccountAction ] BankBalance () () = do useATM Deposit 20 ] -- BankBalance 20 putAction [ Deposit 30 ] -- BankBalance 50 putAction [ ApplyInterest ] -- BankBalance 55 putAction [ Withdraw 10 ] -- BankBalance 45 putAction [ getState $> runUpdate useATM ( BankBalance 0 ) runUpdate useATM ( Deposit 20 , Deposit 30 , ApplyInterest , Withdraw 10 ], BankBalance 45 ) ([],

Hrmm, a bit clunky that we have to wrap every action with a list, but we could pretty easily write a helper putAction' :: MonadUpdate m [p] s => p -> m () to help with that. By running the program we can see that we've collected the actions in the right order and have 'combined' them all by running mappend . We also see that our bank balance ends up where we'd expect! This seems to be pretty similar to the State Monad, we could write helpers that perform each of those actions over the State pretty easily using modify ; but the Update Monad gives us a nice audit log of everything that happened! Not to mention that it limits the available actions to ones that we support; users can't just multiply their bank balance by 100, they have use the approved actions. This means we could verify that actions happened in the correct order, or we could run the same actions over a different starting state and see how it works out!

The Update Monad also has a few tricks when it comes to testing your programs. Since the only thing that can affect our state is a sequence of actions, we can skip all the monad nonsense and test our business logic by just testing that our applyAction function works properly over different lists of actions! Observe:

testBankSystem :: Bool = testBankSystem Deposit 20 , Deposit 30 , ApplyInterest , Withdraw 10 ] ( BankBalance 0 ) applyAction [] ( == BankBalance 45 $> testBankSystem testBankSystem True

Cool stuff! We can write the tests for our business logic without worrying about the impure ways we'll probably be getting those actions (like IO ). This separation makes complicated business logic pretty easy to test, and we can write separate tests for the 'glue' code with confidence that the logic of our actions is correct and that our program CAN'T edit our state in an invalid way since all updates must be performed through the performTransaction function. Note that using an impure base monad like IO could certainly cause the list of actions which are collected to change, but the list of actions which is collected fully describes the state changes which take place; and so testing only the application of actions is sufficient for testing state updates.

There's really only so much we can do with Update alone, but it's pretty easy to write an UpdateT transformer! I'll leave you to check out the implementation here if you like; but this allows us to do things like decide which actions to take based on user input (via IO ), use our state to make choices in the middle of our monad, or use other monads to perform more interesting logic!

Okay! We've got one concrete use-case under our belts and have a pretty good understanding of how all this works! let's see what we can tweak to make things a bit more interesting!

Something that immediately interested me with the update monad is that there are several distinct places to tweak its behaviour without even needing to change which implementation of MonadUpdate we use! We can change the action monoid, or which state we carry, or even our applyAction function! This sort of tweakability leads to all sorts of cool behaviour without too much work, and people can build all sorts of things we didn't initially expect when we wrote the type-classes!

I won't get super in depth on each of these and encourage you to implement them yourself, but here are a few ideas to start with!

Customizations:

Update w () a with applyAction _ () = () A simple implementation of Writer ! The state doesn't matter; only the monoidal actions are tracked!

with Update () r a with applyAction () r = r A simple implementation of Reader ! There're no sensible updates to do; so your state always stays the same.

with Update (Last s) s a with applyAction (Last p) s = fromMaybe s p This is the state monad implemented in Update! get == getState put == putAction . Last . Just modify f == getState >>= putAction . Last . Just . f

with Update (Dual (Endo s)) s a with applyAction (Dual (Endo p)) s = p s Another possible implementation of State inside Update! get == getState put == putAction . Dual . Endo . const modify == putAction . Dual . Endo

with Update Any Bool a with applyAction (Any b) s = b || s You could implement a short-circuiting approach where future actions don't bother running if any previous action has succeeded! You can flip the logic using All and && .

with

Bonus: Performance

The definition of the Update monad given here is quite simple because it's the easiest to explain, but there are a few problems with it; the most notable is that it ONLY passes along the new monoidal sum; NOT the edited state from step to step. In mathematic terms it's still correct since we can compute an up-to-date version of the state; but we have to compute it from scratch every time we run an action! Clearly not great for performance! Like I said earlier you can actually implement a more efficient version of MonadUpdate using State ! We DO still need a dependency on ApplyAction p s though, so keep that in mind. If we have one available we can do something like this:

instance ApplyAction p s => MonadUpdate ( State (p, s)) p s where p s(p, s)) p s = modify (\(p, s) -> (p <> p', applyAction p' s)) putAction p'modify (\(p, s)(pp', applyAction p' s)) = snd <$> get getStateget

Technically we don't even need to keep track of the monoidal sum as we go along; there's no need for it! Unfortunately due to FunctionalDependencies in our MonadUpdate class GHC gets mad if it doesn't show up inside our State Monad somewhere. This implementation keeps track of the latest state and just applies updates as it goes along, giving us a more efficient implementation. Note that using put or modify directly will probably cause some unexpected behaviour in your Update Monad, so you may want to wrap your State in a newtype first to prevent anyone from messing with the internals.

Thanks for reading! I'm not perfect and really just go through all this stuff in my spare time, so if I've missed something (or you enjoyed the post 😄) please let me know! You can find me on Twitter or Reddit!

Hopefully you learned something 🤞! If you did, please consider checking out my book: It teaches the principles of using optics in Haskell and other functional programming languages and takes you all the way from an beginner to wizard in all types of optics! You can get it here. Every sale helps me justify more time writing blog posts like this one and helps me to continue writing educational functional programming content. Cheers!