In the first part of this series, we left off having made states explicit using Haskell data types. We concluded that state transitions were implicit, and that a mistake in implementation, making an erroneous state transition, would not be caught by the type system. We also noted that side effects performed at state transitions complicated testing of the state machine, as we were tied to IO .

Before addressing those problems, let’s remind ourselves of the example state machine diagram. If you haven’t read the previous post, I recommend you go do that first.

Again, the post is a runnable Literate Haskell program. We begin with the language extensions we’ll need, along with the module declaration.

{-# LANGUAGE OverloadedStrings #-} {-# LANGUAGE GADTs #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE TypeFamilies #-} module EnforcingLegalStateTransitions where

Let me quickly explain the GHC language extensions used:

OverloadedStrings converts string literals in Haskell source code to Text values, in our case.

converts string literals in Haskell source code to values, in our case. GADTs enables the use of generalized algebraic data types, an extension to GHC that lets us specify different type signatures for each constructor in a data type. This is useful to parameterize constructors with different types, something we will use for state data types.

enables the use of generalized algebraic data types, an extension to GHC that lets us specify different type signatures for each constructor in a data type. This is useful to parameterize constructors with different types, something we will use for state data types. We use GeneralizedNewtypeDeriving to have our implementation newtype derive instances for Functor , Applicative , and MonadIO . I’ll explain this later in this post.

to have our implementation newtype derive instances for , , and . I’ll explain this later in this post. TypeFamilies extends GHC Haskell with what you can consider type-level functions. We’ll use this extension to associate a concrete state data type with our instance of the state machine.

In addition to Control.Monad.IO.Class , we import the same modules as in the previous post.

import Control.Monad.IO.Class import Data.List.NonEmpty import Data.Semigroup import qualified Data.Text.IO as T

From the modules specific to the blog post series we import some functions and data types. As before, their exact implementations are not important. The ConsoleInput module provides helpers for retrieving text input and confirmation from the terminal.

import qualified PaymentProvider import Checkout ( Card (..) (..) , CartItem , OrderId ( .. ) , mkItem , calculatePrice , newOrderId ) import qualified ConsoleInput

Enough imports, let’s go build our state machine!

The State Machine Protocol

In contrast to the transition function in the previous post, where a single function had the responsibility of deciding which state transitions were legal, performing associated side effects on transitions, and transitioning to the next state, we will now separate the protocol from the program. In other words, the set of states, and the associated state transitions for certain events, will be encoded separately from the implementation of the automaton. Conceptually, it still follows the definition we borrowed from Erlang:

State(S) × Event(E) → Actions (A), State(S’)

With the risk of stretching a metaphor too thin, I like to think of the split representation as taking the single-function approach and turning it inside-out. Our program is separate from our state machine protocol, and we can implement it however we like, as long as we follow the protocol.

Empty Data Types for States

Our states are no longer represented by a single data type with constructors for each state. Instead, we create an empty data type for each state. Such a type has no constructors, and is therefore not inhabited by any value. We will use them solely as markers in GADT constructors, a technique in general known as phantom types.

data NoItems data HasItems data NoCard data CardSelected data CardConfirmed data OrderPlaced

I know that phantom types and GADTs sound scary at first, but please hang in there, as I’ll explain their practical use throughout this post, and hopefully give you a sense of why we are using them.

State Machine Protocol as a Type Class

We encode our state machine protocol, separate from the program, using a type class.

class Checkout m where

In our protocol, we do not want to be specific about what data type is used to represent the current state; we only care about the state type it is parameterized by. Therefore, we use an associated type alias, also known as an open type family, with kind * -> * to represent states.

type State m :: * -> *

The signature * -> * can be thought of as a type-level function, or a type constructor, from type to type (the star is the kind of types). The parameter m to state means we are associating the state type with the instance of m , so that different instances can specify their own concrete state types.

There are two benefits of using an associated type for state:

Different instances of Checkout can provide different concrete state data types, hiding whatever nasty implementation details they need to operate, such as database connections, web sessions, or file handles. The concrete state type is not known when using the state machine protocol, and it is therefore impossible to create a state “manually”; the program would not typecheck.

In our case, the parameter will always be one of our empty data types declared for states. As an example, (State m NoItems) has kind * , and is used to represent the “NoItems” state abstractly.

Note that m also has kind * -> * , but not for the same reason; the m is going to be the monadic type we use for our implementation, and is therefore higher-kinded.

Events as Type Class Methods

Checkout specifies the state machine events as type class methods, where method type signatures describe state transitions. The initial method creates a new checkout, returning a “NoItems” state. It can be thought of as a constructor in object-oriented programming terms.

initial :: m ( State m NoItems ) m (

The value returned, of type (State m NoItems) , is the first state. We use this value as a parameter to the subsequent event, transitioning to another state. Events that transition state from one to another take the current state as an argument, and return the resulting state.

The select event is a bit tricky, as it is accepted from both “NoItems” and “HasItems”. We use the union data type SelectState , analogous to Either , that represents the possibility of either “NoItems” or “HasItems”. The definition of SelectState is included further down this post.

select :: SelectState m -> CartItem -> m ( State m HasItems ) m (

Worth noting is that the resulting state is returned inside m . We do that to enable the instance of Checkout to perform computations available in m at the state transition.

Does this ring a bell?

Just as in the previous post, we want the possibility to interleave side effects on state transitions, and using a monadic return value gives the instance that flexibility.

The checkout event is simpler than select , as it transitions from exactly one state to another.

checkout :: State m HasItems -> m ( State m NoCard ) m (

Some events, like selectCard , carry data in the form of arguments, corresponding to how some event data constructors had arguments. Most of the events in Checkout follow the patterns described so far.

selectCard :: State m NoCard -> Card -> m ( State m CardSelected ) m ( confirm :: State m CardSelected -> m ( State m CardConfirmed ) m ( placeOrder :: State m CardConfirmed -> m ( State m OrderPlaced ) m (

Note that we are not doing any error handling. All operations return state values. In a real-world system you might need to handle error cases, like selectCard not accepting the entered card number. I have deliberately excluded error handling from this already lengthy post, but I will probably write a post about different ways of handling errors in this style of state machine encoding.

Similar to select , the cancel event is accepted from more than one state. In fact, it is accepted from three states: “NoCard”, “CardSelected”, and “CardConfirmed”. Like with select , we use a union data type representating the ternary alternative.

cancel :: CancelState m -> m ( State m HasItems ) m (

Finally, we have the end method as a way of ending the state machine in its terminal state, similar to a destructor in object-oriented programming terms. Instances of Checkout can have end clean up resources associated with the machine.

end :: State m OrderPlaced -> m OrderId

As promised, I will show you the definitions of SelectState and CancelState , the data types that represent alternative source states for the select and cancel events, respectively.

data SelectState m = NoItemsSelect ( State m NoItems ) | HasItemsSelect ( State m HasItems ) data CancelState m = NoCardCancel ( State m NoCard ) | CardSelectedCancel ( State m CardSelected ) | CardConfirmedCancel ( State m CardConfirmed )

Each constructor takes a specific state as argument, thus creating a union type wrapping the alternatives.

A Program using the State Machine Protocol

Now that we have a state machine protocol, the Checkout type class, we can write a program with it. This is the automaton part of our implementation, i.e. the part that drives the state machine forward.

As long as the program follows the protocol, it can be structured however we like; we can drive it using user input from a console, by listening to a queue of commands, or by incoming HTTP requests from a web server. In the interest of this post, however, we will keep to reading user input from the console.

The type signature of fillCart constrains m to be an instance of both Checkout and MonadIO . Moreover, it is a function from a “NoItems” state to a “HasItems” state. The type is similar to the event methods’ type signatures in the Checkout protocol, and similarly describes a state transition with a type.

fillCart :: ( Checkout m, MonadIO m) m,m) => State m NoItems -> m ( State m HasItems ) m (

This is where we are starting to use the MTL style of abstracting effects, and combining different effects by constraining the monadic type with multiple type classes.

The critical reader might object to using MonadIO , and claim that we have not separated all side effects, and failed in making the program testable. They wouldn’t be wrong. I have deliberately left the direct use of MonadIO in to keep the example somewhat concrete. We could refactor it to depend on, say, a UserInput type class for collecting more abstract user commands. By using MonadIO , though, the example highlights particularly how the state machine protocol has been abstracted, and how the effects of state transitions are guarded by the type system, rather than making everything abstract. I encourage you to try out both approaches in your code!

The definition of fillCart takes a “NoItems” state value as an argument, prompts the user for the first cart item, selects it, and hands off to selectMoreItems .

= fillCart noItems <$> ConsoleInput.prompt "First item:" mkItemConsoleInput.prompt >>= select ( NoItemsSelect noItems) select (noItems) >>= selectMoreItems selectMoreItems

The event methods of the Checkout protocol, and functions like fillCart and selectMoreItems , are functions from one state to a monadic return value of another state, and thus compose using (>>=) .

The selectMoreItems function remains in a “HasItems” state. It asks the user if they want to add more items. If so, it asks for the next item, selects that and recurses to possibly add even more items; if not, it returns the current “HasItems” state. Note how we need to wrap the “HasItems” state in HasItemsSelect to create a SelectState value.

selectMoreItems :: ( Checkout m, MonadIO m) m,m) => State m HasItems -> m ( State m HasItems ) m ( = do selectMoreItems s <- ConsoleInput.confirm "More items?" moreConsoleInput.confirm if more more then <$> ConsoleInput.prompt "Next item:" mkItemConsoleInput.prompt >>= select ( HasItemsSelect s) select (s) >>= selectMoreItems selectMoreItems else return s

When all items have been added, we are ready to start the checkout part. The type signature of startCheckout tells us that it transitions from a “HasItems” state to an “OrderPlaced” state.

startCheckout :: ( Checkout m, MonadIO m) m,m) => State m HasItems -> m ( State m OrderPlaced ) m (

The function starts the checkout, prompts for a card, and selects it. It asks the user to confirm the use of the selected card, and ends by placing the order. If the user did not confirm, the checkout is canceled, and we go back to selecting more items, followed by attempting a new checkout.

= do startCheckout hasItems <- checkout hasItems noCardcheckout hasItems <- ConsoleInput.prompt "Card:" cardConsoleInput.prompt <- selectCard noCard ( Card card) cardSelectedselectCard noCard (card) <- ConsoleInput.confirm ( "Confirm use of '" <> card <> "'?" ) useCardConsoleInput.confirm (card if useCard useCard then confirm cardSelected >>= placeOrder confirm cardSelectedplaceOrder else cancel ( CardSelectedCancel cardSelected) >>= cancel (cardSelected) >>= selectMoreItems startCheckout

The protocol allows for cancellation in all three checkout states, but that the program only gives the user a possibility to cancel in the end of the process. Again, the program must follow the rules of the protocol, but it is not required to trigger all events the protocol allows for.

The definition of checkoutProgram is a composition of what we have so far. It creates the state machine in its initial state, fills the shopping cart, starts the checkout, and eventually ends the checkout.

checkoutProgram :: ( Checkout m, MonadIO m) m,m) => m OrderId = checkoutProgram >>= fillCart >>= startCheckout >>= end initialfillCartstartCheckoutend

We now have a complete program using the Checkout state machine protocol. To run it, however, we need an instance of Checkout .

Defining an Instance for Checkout

To define an instance for Checkout , we need a type to define it for. A common way of defining such types, especially in MTL style, is using newtype around a monadic value. The type name often ends with T to denote that it’s a transformer. Also by convention, the constructor takes a single-field record, where the field accessor follows the naming scheme run<TypeName> ; in our case runCheckoutT .

newtype CheckoutT m a = CheckoutT m a { runCheckoutT :: m a m a } deriving ( Functor , Monad , Applicative , MonadIO )

We derive the MonadIO instance automatically, along with the standard Functor , Applicative , and Monad hierarchy.

Monad Transformer Stacks and Instance Coupling

Had we not derived MonadIO , the program from before, with constraints on both Checkout and MonadIO , would not have compiled. Therein lies a subtle dependency that is hard to see at first, but that might cause you a lot of headache. Data types used to instantiate MTL style type classes, when stacked, need to implement all type classes in use. This is caused by the stacking aspect of monad transformers, and is a common critique of MTL style.

Other techniques for separating side effects, such as free monads or extensible effects, have other tradeoffs. I have chosen to focus on MTL style as it is widely used, and in my opinion, a decent starting point. If anyone rewrites these examples using another technique, please drop a comment!

A Concrete State Data Type

Remember how we have, so far, only been talking about state values abstractly, in terms of the associated type alias State in the Checkout class? It is time to provide the concrete data type for state that we will use in our instance.

As discussed earlier, the type we associate for State need to have kind (* -> *) . The argument is the state marker type, i.e. one of the empty data types for states. We define the data type CheckoutState using a GADT, where s is the state type.

data CheckoutState s where

With GADTs , data constructors specify their own type signatures, allowing the use of phantom types, and differently typed values resulting from the constructors. Each constructor parameterize the CheckoutState with a different state type.

The NoItems constructor is nullary, and constructs a value of type CheckoutState NoItems .

NoItems :: CheckoutState NoItems

The data constructor NoItems is defined here, whereas the type NoItems is defined in the beginning of the program, and they are not directly related.

There is, however, a relation between them in terms of the CheckoutState data type. If we have a value of type CheckoutState NoItems , and we pattern match on it, GHC knows that there is only one constructor for such a value. This will become very handy when defining our instance.

The other constructors are defined similarly, but some have arguments, in the same way the State data type in the previous post had. They accumulate the extended state needed by the state machine, up until the order is placed.

HasItems :: NonEmpty CartItem -> CheckoutState HasItems NoCard :: NonEmpty CartItem -> CheckoutState NoCard CardSelected :: NonEmpty CartItem -> Card -> CheckoutState CardSelected CardConfirmed :: NonEmpty CartItem -> Card -> CheckoutState CardConfirmed OrderPlaced :: OrderId -> CheckoutState OrderPlaced

We have a concrete state data type, defined as a GADT, and we can go ahead defining the instance of Checkout for our CheckoutT newtype. We need MonadIO to perform IO on state transitions, such as charging the customer card.

instance ( MonadIO m) => Checkout ( CheckoutT m) where m)m)

Next, we can finally tie the knot, associating the state type for CheckoutT with CheckoutState .

type State ( CheckoutT m) = CheckoutState m)

We continue by defining the methods. The initial method creates the state machine by returning the initial state, the NoItems constructor.

= return NoItems initial

In select , we receive the current state, which can be either one of the constructors of SelectState . Unwrapping those gives us the CheckoutState value. We return the HasItems state with the selected item prepended to a non-empty list.

= select state item case state of state NoItemsSelect NoItems -> return ( HasItems (item :| [])) (item[])) HasItemsSelect ( HasItems items) -> items) return ( HasItems (item <| items)) (itemitems))

As emphasized before, GHC knows which constructors of CheckoutState can occur in the SelectState wrappers, and we can pattern match exhaustively on only the possible state constructors.

The checkout , selectCard , and confirm methods accumulate the extended state, and returns the appropriate state constructor.

HasItems items) = checkout (items) return ( NoCard items) items) NoCard items) card = selectCard (items) card return ( CardSelected items card) items card) CardSelected items card) = confirm (items card) return ( CardConfirmed items card) items card)

Now for placeOrder , where we want to perform a side effect. We have constrained m to be an instance of MonadIO , and we can thus use the effectful newOrderId and PaymentProvider.chargeCard in our definition.

CardConfirmed items card) = do placeOrder (items card) <- newOrderId orderIdnewOrderId let price = calculatePrice items pricecalculatePrice items PaymentProvider.chargeCard card price return ( OrderPlaced orderId) orderId)

Similar to select , cancel switches on the alternatives of the CancelState data type. In all cases it returns the HasItems state with the current list of items.

= cancel cancelState case cancelState of cancelState NoCardCancel ( NoCard items) -> items) return ( HasItems items) items) CardSelectedCancel ( CardSelected items _) -> items _) return ( HasItems items) items) CardConfirmedCancel ( CardConfirmed items _) -> items _) return ( HasItems items) items)

Finally, the definition of end returns the generated order identifier.

OrderPlaced orderId) = return orderId end (orderId)orderId

The CheckoutT instance of Checkout is complete, and we are ready to stitch everything together into a running program.

Putting the Pieces Together

To run checkoutProgram , we need an instance of Checkout , and an instance of MonadIO . There is already an instance (MonadIO IO) available. To select our CheckoutT instance for Checkout , we use runCheckoutT .

example :: IO () () = do example OrderId orderId <- runCheckoutT checkoutProgram orderIdrunCheckoutT checkoutProgram "Completed with order ID: " <> orderId) T.putStrLn (orderId)

The complete checkout program is run, using the CheckoutT instance, and an OrderId is returned, which we print at the end. A sample execution of this program looks like this:

λ> example First item: Banana More items? (y/N) y Next item: Horse More items? (y/N) y Next item: House More items? (y/N) n Card: 0000-0000-0000-0000 Confirm use of '0000-0000-0000-0000'? (y/N) y Charging card 0000-0000-0000-0000 $200 Completed with order ID: foo

Cool, we have a console implementation running!

Instances Without Side Effects

A benefit of using MTL style, in addition to have effects be explicit, is that we can write alternative instances. We might write an instance that only logs the effects, using a Writer monad , collecting them as pure values in a list, and use that instance when testing the state machine.

Parting Thoughts

Using a sort of extended MTL style, with conventions for state machine encodings, gives us more type safety in state transitions. In addition to having turned our state machine program inside-out, into a protocol separated from the automaton, we have guarded side effects with types in the form of type class methods. Abstract state values, impossible to create outside the instance, are now passed explicitly in state transitions.

But we still have a rather loud elephant in the room.

Suppose I’d write the following function, wherein I place the order twice. Do you think it would typecheck?

doBadThings :: ( Checkout m, MonadIO m) m,m) => State m CardConfirmed -> m ( State m OrderPlaced ) m ( = do doBadThings cardConfirmed _ <- placeOrder cardConfirmed placeOrder cardConfirmed placeOrder cardConfirmed

The answer is yes, it would typecheck. With the Checkout instance we have, the customer’s card would be charged twice, without doubt departing from our business model, and likely hurting our brand.

The problem is that we are allowed to discard the state transitioned to, a value of type (State m OrderPlaced) , returned by the first placeOrder expression. Then, we can place the order again, using the old state value of type (State m CardConfirmed) . The ability to reuse, or never use, state values is the Achilles’ heel of this post’s state machine encoding.

We could venture into the land of linear types, a feature recently proposed to be added to GHC. With linear types, we could ensure state values are used exactly once, making our current approach safer.

I’d like to step back for a moment, however, and remind you that the techniques we encounter along this journey are not ordered as increasingly “better”, in terms of what you should apply in your work. I show more and more advanced encodings, using various GHC language extensions, but it doesn’t mean you should necessarily use the most advanced one. Simplicity is powerful, something Tim Humphries tweet thread reminded me about this morning, and I recommend you start out simple.

As demonstrated, the extended MTL style for encoding state machines presented in this post has a type safety flaw. That doesn’t mean the technique is useless and should be forever rejected. At least not in my opinion. It gives additional type safety around state transitions, it composes well with MTL style programs in general, and it uses a modest collection of type system features and language extensions. We can write alternative instances, without any IO, and use them to test our state machines programs in a pure setting.

If you still feel that all hope is lost, then I’m happy to announce that there will be more posts coming in this series! To demonstrate a possible next step, in terms of even greater type safety, in the next post we will be exploring indexed monads and row kinds as a way of armoring the Achilles heel.

Happy hacking!

Revisions