FRP made simple!

Posted on May 5, 2015 by Travis Athougies

Functional Reactive Programming refers to a declarative way to write eventful program in functional languages like Haskell. While the concepts behind FRP are straightforward to grasp, the actual implementations are not.

Libraries like sodium, reactive-banana, elerea, and yampa share one thing in common: large code bases that are not easily understood by beginners. In this blog post, I will develop a sodium-like FRP implementation for Haskell in under 200 lines. Note that this is possible by eschewing any concerns over performance or memory usage, so this library would probably not be practical to use in any live code.

A brief recap of FRP

FRP libraries usually consist of two primitives: Event and Behavior. An Event refers to values which only occur at certain times, and which are lost after the moment they occur. You can think of an Event a as a list of pairs of times t and values a: [(t, a)]. A Behavior is a continuous value. Here, continuous means that a Behavior has a defined value for any given time (not the mathematical definition!). We can think of a Behavior a as a function from a time t to the value a.

First steps

The first thing we will do is define a monad for all of our FRP computations. Our FRP implementation will also allow us to execute arbitrary IO actions in response to events (much like the execute primitive in Sodium), so we will need a separate monad to represent what happens “in the moment.” Right now, we’ll just make both of these monads aliases for IO.

Also, before we start, the final library we will end up writing is available on GitHub.

In many FRP implementations, Behavior actually isn’t anything more than a step function whose value can only be updated by events. Therefore, we’ll start by defining our Event type. The only really important thing about events is that we be able to subscribe and unsubscibe from them.

First, we define the RegisterEventListener type. Functions of this type can be used to register a function in the Moment monad which will be called whenever a new value (of type a) of the event is produced. The listener must be registered in the IO monad, and the registration function returns another monadic action of type IO (). This function is used to deregister the listener that has been added.

Event a fills the requirements for both a Monoid and a Functor. For the Monoid instance, mempty is the Event that never fires, and mappend takes two Event a’s and returns a new Event a that fires whenever either of the first two fires.

Let’s look at each function individually. First, let’s consider the mempty or never functions. Whenever a listener tries to register with this event, the listener is totally ignored and the deregistration function returned does nothing. This means that the listener will never be called (i.e., the event will never fire), which is the behavior we wanted.

The mappend or merge function take two events and returns a new one. When a listener tries to register with the new event, the listener is in fact registered with both events. Thus, the listener will be called whenever either a or b fire, which is the behavior we wanted. The deregistration function deregisters the listener from both a and b.

Now for the Functor instance. The Functor instance for event allows us to apply functions to the value contained inside an event in order to a get a new dependent event. I’ve included a type signature to remind the reader of the type of the fmap function for Event’s.

In fmap, we translate our listening function to be able to listen to the original event, and then subscribe it to that. Recall that eb has type Event b, so _eventRegisterListener eb has type (b -> Moment ()) -> IO (IO ()). The listener function we’ve been supplied has type a -> Moment (), but listener . f has type b -> Moment(), so we can use it as the new listener for eb.

Getting behaviors to behave

As we said before, although we can think of Behaviors as continuous-time properties, in reality, our FRP implementation will treat them as stepper functions that must be updated by an event. Therefore, every Behavior will need to be tied with an event that will fire whenever the behavior is updated. Secondly, although the value of Behavior is impure (it changes through time), its value is well defined given a point in time. Therefore, we should be able to access it through Moment. Thus, our Behavior type will also have to support a way to get at its current value.

It’s easy to combine these requirements into a Behavior data type, which consists of an event which fires on updates and a function to get the current value.

It turns out that Behavior is an Applicative. The pure function for Behavior will return a Behavior whose value does not change in time. The (<*>) function will return a Behavior whose value at any given point in time is the current value of the first Behavior applied to the current value of the second. I’ve included type signatures for convenience.

The Behavior returned by pure a is a Behavior that never updates (i.e., is constant) and whose value always returns a. This fulfills our requirements for a constant Behavior.

The Behavior returned by f <*> a is a Behavior who updates whenever f or a update, and whose value is the value of f applied to the value of a.

Interfacing with the real world

So now we have Event and Behavior data type and methods to combine them with other Event’s and Behavior’s respectively. What we don’t have is any way to make an Event that actually fires, or a Behavior that is not built from other Behavior’s. Let’s implement this functionality to make our library actually useful.

Putting the ‘R’ in FRP

Most FRP implementations offer the user a way to create an event as well as an IO (or equivalent) function that can be used to trigger that event. Our library is no different. Let’s define a newEvent function which will return a new Event as well as a function to trigger our Event.

I won’t go into all the details here, but basically, the sausage is made in the newEventRegistration function which uses an IORef to keep track of registered listeners and a Unique from the Data.Unique library to give each listener a unique key, which is useful for the deregistration function.

Hold your horses!

So now we can make an Event that we can fire by simply calling a function, but how do we make a Behavior? Simple! Since Behavior’s are just steppers updated by events, we’ll write a function that will take an Event a and give us a Behavior a. However, because the Event a might not fire immediately, we’ll need to give the Behavior a an initial a to hold in the mean-time.

Again, I won’t go into too much detail here, but basically we create an IORef to store the current value of the behavior, which we read out in the sampling function. The behavior updates event is made by creating an Event () from the Event a supplied to us (i.e., the behavior will update whenever the event does). The (<$) function is given to us for free by virtue of Event being a Functor. Finally, we register a listener for updates to the updating event, and update the cell appropriately. We use the addFinalizer function from System.Mem.Weak to automatically unregister our listener when behavior is garbage collected.

There is one more consideration that we’re not taking into account here. Because we write to the IORef immediately in the listener, it is possible that sampling the behavior at different points in the Moment monad will give us different results. This is not what we want since a computation in the moment monad should semantically run “at the same time.” What we really want is for the cell to be written after the current moment is complete. We can do this by updating our definition of the Moment monad.

We will also need a function that can run a Moment a inside IO.

Note that we will also have to update our use of Moment elsewhere, including adding liftIO’s as necessary. All of these changes are made in the GitHub source.

Wrapping things up in the real world

Finally, let’s create some utility functions to listen to Event’s and Behavior’s, thus completing our interactions with the real world. This will also allow us to keep our implementations of Event and Behavior opaque.

Again, the functions here are pretty self-explanatory, except perhaps listenToBehavior. Basically, we call the handler once with the initial sampled value of bb and then we register to updates from bb and call the handler using the value of bb.

This all looks good, but there’s a problem with sampling bb in the _behaviorUpdates bb handler. Remember how we took care to make sure that Behavior’s do not update until the current Moment is complete? This means that when we sample bb here we will be getting the old value. That’s not what we want! To remedy this, we’ll use the same trick we used with hold. We will modify Moment to keep track of IO actions to run after all the holds have been updated.

Again, previous Moment usages will have to be updated to reflect this new structure, which has been done in the GitHub source.

Some other primitives

Modern FRP libraries usually contain a few other primitives, which we’ll implement here. One of the more important features is the ability to define accumulators, Event’s whose value depends on previous values. Let’s write the accum primitive now.

Keeping state

For accum we create an IORef to hold the current value, and then subscribe to the updaters event directly. We must subscribe to updaters here, which means that accum must be run in the React monad. If we didn’t subscribe to updaters immediately, then it’s possible that we will miss some updates. We also play nice by registering a finalizer on the new event we create.

Using accum and hold we can make a Behavior a whose current value depends on previous ones as well.

Spilling the beans

reactive-banana and sodium also have a primitive that takes an event whose value is a list and returns an event of just values that fires for each element in the list. This is called something like spill.

Simply put, when someone listens to the spilled event, we subscribe to the Event [a] and call the listener for Event a on each element of the list.

Calming down

spill gave us the ability to have one event produce multiple simultaneous events, but what if we don’t want to listen to all of those? What if we wanted to “un-spill”? Luckily, there’s a combinator for that, usually called calm. It takes an event that may fire more than once in a given Moment and returns a new Event that will only fire once for the first firing of the event. We will need to modify the Moment monad again to keep track of which events have already been delivered.

Basically, calm adapts the listener to check if the listener has been called yet in this moment. If it has not, then it calls the listener and notes in this Moment that the listener has been called. Sweet!

Switching things up

The most complicated combinator we’re going to write today is switchE and switch. These combinators let you dynamically change the state of the network. The switchE combinator takes a Behavior (Event a) and returns an Event a that fires whenever the current Event a in the given Behavior fires. This requires some trickery, but we actually don’t need to write this in the React monad.

Basically, what this does is that, whenever a new listener subscribes, we subscribe to the current Event a contained in the behavior, as well as the Behavior updates. The subscription to the Event a will call our listener for events on the initial event until the Behavior updates. At this point, the switchToNewEvent is called after all the new behavior values have been written in the holds (that’s why we use tell to register an afterHolds action). Because switchToNewEvent is called after the current Moment, the listener will continue to be called on firings of the old event until the end of the Moment. This is what we want, since our convention is that Behavior’s do not update until the end of the current moment.

When switchToNewEvent is called, we read the current unregistration IO action from the IORef and run it, thus disconnecting listener from future firings of the old Event a. We read the new Event a from the behavior and register listener to listen to the new event instead. Finally, we write out the deregistration function for the new event fo the IORef. The deregistration function for the dynamically switched event disconnects both the listener from the current event and switchToNewEvent from the behavior updates.

Whereas switchE dynamically switches between Event a’s, switch dynamically switches between Behavior’s. Remember, we need to two things to define a behavior: an event that updates when the behavior updates, and a way to read the current value. The switchE gives us an easy way to express the former, and it’s pretty straightforward to write a function to read the current value.


And that’s it! In this post, we’ve written a simple FRP implementation in less than 200 lines. Unlike it’s more complicated cousins, like reactive-banana or sodium, this implementation makes no performance or memory guarantees, and doesn’t even have very strict semantics, but you’ll find it works pretty intuitively for most things (TM). In the next tutorial, we’ll use our library to write a simple game. Stay tuned for more, and be sure to check out the GitHub source for React.Hs.