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An asynchronous effect monad for PureScript.

The moral equivalent of ErrorT (ContT Unit (Eff e)) a, for effects e.

Aff lets you say goodbye to monad transformers and callback hell!

main = launchAff do
  response <- Ajax.get ""
  liftEff $ log response.body

See the tests for more examples.


bower install purescript-aff


An example of Aff is shown below:

deleteBlankLines path = do
  contents <- loadFile path
  let contents' = S.join "\n" $ A.filter (\a -> S.length a > 0) (S.split "\n" contents)
  saveFile path contents'

This looks like ordinary, synchronous, imperative code, but actually operates asynchronously without any callbacks. Error handling is baked in so you only deal with it when you want to.

The library contains instances for Semigroup, Monoid, Apply, Applicative, Bind, Monad, Alt, Plus, MonadPlus, MonadEff, and MonadError. These instances allow you to compose asynchronous code as easily as Eff, as well as interop with existing Eff code.

Escaping Callback Hell

Hopefully, you're using libraries that already use the Aff type, so you don't even have to think about callbacks!

If you're building your own library, or you have to interact with some native code that expects callbacks, then purescript-aff provides a makeAff function:

makeAff :: forall e a. ((Error -> Eff e Unit) -> (a -> Eff e Unit) -> Eff e Unit) -> Aff e a

This function expects you to provide a handler, which should call a user-supplied error callback or success callback with the result of the asynchronous computation.

For example, let's say we have an AJAX request function that expects a callback:

exports.ajaxGet = function(callback) { // accepts a callback
  return function(request) { // and a request
    return function() { // returns an effect
      doNativeRequest(request, function(response) {
        callback(response)(); // callback itself returns an effect
foreign import ajaxGet :: forall e. (Response -> Eff e Unit) -> Request -> Eff e Unit

We can wrap this into an asynchronous computation like so:

ajaxGet' :: forall e. Request -> Aff e Response
ajaxGet' req = makeAff (\error success -> ajaxGet success req)

This eliminates callback hell and allows us to write code simply using do notation:

do response <- ajaxGet' req
   liftEff $ log response.body


All purely synchronous computations (Eff) can be lifted to asynchronous computations with liftEff defined in Control.Monad.Eff.Class (see here).

import Control.Monad.Eff.Class

liftEff $ log "Hello world!"

This lets you write your whole program in Aff, and still call out to synchronous code.

If your Eff code throws exceptions (err :: Exception), you can remove the exceptions using liftEff', which brings exceptions to the value level as an Either Error a:

do e <- liftEff' myExcFunc
   liftEff $ either (const $ log "Oh noes!") (const $ log "Yays!") e

Dealing with Failure

The Aff monad has error handling baked in, so ordinarily you don't have to worry about it.

When you need to deal with failure, you have several options.

  1. Attempt
  2. Alt
  3. MonadError

1. Attempt

If you want to attempt a computation but recover from failure, you can use the attempt function:

attempt :: forall e a. Aff e a -> Aff e (Either Error a)

This returns an Either Error a that you can use to recover from failure.

do e <- attempt $ Ajax.get ""
   liftEff $ either (const $ log "Oh noes!") (const $ log "Yays!") e

2. Alt

Because Aff has an Alt instance, you may also use the operator <|> to provide an alternative computation in the event of failure:

do result <- Ajax.get "" <|> Ajax.get ""
   return result

3. MonadError

Aff has a MonadError instance, which comes with two functions: catchError, and throwError.

These are defined in purescript-transformers. Here's an example of how you can use them:

do resp <- (Ajax.get "") `catchError` (const $ pure defaultResponse)
   if resp.statusCode != 200 then throwError myErr
   else pure resp.body

Thrown exceptions are propagated on the error channel, and can be recovered from using attempt or catchError.


Using the forkAff, you can "fork" an asynchronous computation, which means that its activities will not block the current thread of execution:

forkAff myAff

Because Javascript is single-threaded, forking does not actually cause the computation to be run in a separate thread. Forking just allows the subsequent actions to execute without waiting for the forked computation to complete.

If the asynchronous computation supports it, you can "kill" a forked computation using the returned canceler:

canceler <- forkAff myAff
canceled <- canceler `cancel` (error "Just had to cancel")
_        <- liftEff $ if canceled then (log "Canceled") else (log "Not Canceled")

If you want to run a custom canceler if some other asynchronous computation is cancelled, you can use the cancelWith combinator:

otherAff `cancelWith` myCanceler


The Control.Monad.Aff.AVar module contains asynchronous variables, which are very similar to Haskell's MVar construct. These can be used as low-level building blocks for asynchronous programs.

do v <- makeVar
   forkAff (later $ putVar v 1.0)
   a <- takeVar v
   liftEff $ log ("Succeeded with " ++ show a)

You can use these constructs as one-sided blocking queues, which suspend (if necessary) on take operations, or as asynchronous, empty-or-full variables.

Parallel Execution

There are MonadPar and MonadRace instances defined for Aff, allowing for parallel execution of Aff computations.

There are two ways of taking advantage of these instances - directly through the par and race functions from these classes, or by using the Parallel newtype wrapper that enables parallel behaviours through the Applicative and Alternative operators.

In the following example, using the newtype, two Ajax requests are initiated simultaneously (rather than in sequence, as they would be for Aff):

runParallel (f <$> parallel (Ajax.get "") <*> parallel (Ajax.get ""))

And the equivalent using the MonadPar function directly:

par f (Ajax.get "") (Ajax.get "")

The race function from MonadPar or the (<|>) operator of the Alt instance of Parallel allows you to race two asynchronous computations, and use whichever value comes back first (or the first error, if both err).

The runParallel function allows you to unwrap the Aff and return to normal monadic (sequential) composition.

A parallel computation can be canceled if both of its individual components can be canceled.

API documentation is published on Pursuit.

A good overview of Aff was provided during LambdaConf 2015 conference