DataDriven

Besides this wiki page, here are more ways to find out about DataDriven.

• Read the Haddock docs (with source code, additional examples, and Comment/Talk links).
• Get the code repository: darcs get http://darcs.haskell.org/packages/DataDriven, or
• Grab a distribution tarball.
• See the version history.
• See the use of events and sources in Phooey and Eros.

Please leave comments at the Talk page.

2 Events

2.1 Background

The heart of the library is a notion of functional, composable events, with a data-driven implementation. Most of the ideas and vocabulary are borrowed from Fran, when Fran's events came to mean multiple occurrences (see Declarative Event-Oriented Programming, rather than the initial ICFP '97 publication). As in Fran, you can think of an event as a stream of "occurrences", each of which has a time and a value. The implementation, however, is radically different from Fran's, being data-driven rather than demand-driven. And in some cases, the functions are not pure. There are also several event-related functions, to create time-varying values.

2.2 A first look at the interface

Some of the useful event operations come through standard classes.

• Functor
  :
  fmap f e
  is the event that occurs whenever
  e
  occurs, but whose occurrence values come from applying
  f
  to the values from
  e
  . (Fran's
  (==>)
  .)
• Monoid
  :
  mempty
  is the event that never occurs, and
  e `mappend` e'
  is the event that combines occurrences from
  e
  and
  e'
  . (Fran's
  neverE
  and
  (.|.)
  .)
• Monad
  :
  return a
  is an event with a single occurrence. This one doesn't quite fit the original semantics, as the occurrence is delivered immediately on "listening" to an event (discussed later). In
  e >>= f
  , each occurrence of
  e
  leads, through
  f
  , to a new event. Similarly for
  join ee
  , which is somehow simpler for me to think about. The occurrences of
  e >>= f
  (or
  join ee
  ) correspond to the union of the occurrences of all such events. For example, suppose we're playing Asteroids and tracking collisions. Each collision can break an asteroid into more of them, each of which has to be tracked for more collisions. Another example: A chat room has an "enter" event, whose occurrences contain new events like "speak".

As a simple example, the following function transforms and combines two events:

show2 :: (Show a, Show b) => Event a -> Event b -> Event String
show2 ea eb = showE ea `mappend` showE eb
 where
   showE e = fmap show e

2.3 Events as continuations

newtype Cont o a = Cont { runCont :: (a -> o) -> o }

type Event = Cont (IO ())

• Subscribing
  l
  to
  mempty
  has no effect, since the
  mempty
  is guaranteed never to occur.
• Subscribing
  l
  to
  ea `mappend` eb
  subscribes
  l
  to each of
  ea
  and
  eb
  .
• Subscribing
  l
  to
  fmap f e
  subscribes
  l . f
  to
  e
  .
• Subscribing
  l
  to
  join e
  subscribes to
  e
  a listener that subscribes to every event generated by
  e
  . (Similarly for
  e >>= f == join (fmap f e)
  .)

3 Sources

3.1 Composing Sources

Like events, sources have a more general, and surprisingly simple, form:

type SourceG change m = ((,) change) `O` m

\begin{code} type SourceG' change m a = (change, m a) \end{code}

type SourceC o m = SourceG (Cont (m o) ()) m

Still more specifically

type Source = SourceC () IO

3.2 Sources and events

As an example of event-based sources, the following function makes a source with an initial value and changing at every occurrence of an event. The resulting source remembers the event's most recent occurrence value.

mkStepper :: a -> Event a -> IO (Source a)

mkAccumS :: a -> Event (a -> a) -> IO (Source a)

snapshot :: Event a -> Source b -> Event (a,b)

4 Ephemeral values

4.1 GC favors demand-driven computation

The purpose of garbage collection is to keep services alive as long as they are useful to clients and then free up the services' computational resources (effort and memory). Conventional garbage collection works very well for demand-driven (pull-based) computation, but not for data-driven (push-based) computation.

Consider a piece of information supplied by a service and used by a client. In a demand-driven scenario, the client has a pointer to the service and uses that pointer to get more of the information. The client keeps the serice alive. When the client get GC'd, its pointer to the service goes away. If there are no more pointers to the service, then it will also get GC'd. Both the computational effort and the memory are freed up for other uses. GC did its job.

The situation is reversed for data-driven computation. Here, the service pushes information to the client, so the service has a pointer to the client. This pointer means that the service keeps the client alive and keeps computing even when the client is no longer of any use. GC fails to satisfy its purpose.

4.2 Caching and weak pointers

Various forms of caching have this same problem. Suppose we use a hash table to memoize an expensive function. Even though the hash table is in service to function's arguments, the table's key/value entries keep the key values (function arguments) from ever getting reclaimed. Ideally, the situation would be reversed: the key would keep its table entry alive, and when the key was GC'd, they entry would shortly follow. Unfortunately, the direction of pointers, from entry to key, means that the entries keep the values alive, wasting space and slowing down search for useful entries.

4.3 Ephemeral listener

What does all this have to events and sources? Recall that an event is simply a means of registering a "listener" (continuation) to be invoked on every occurrence. The event must have some kind of reference to the listener in order to invoke it. It must not keep the listener alive, however, since the event is the service and the listener is the client. The solution is for events to hold their clients weakly, i.e., point to them via weak references. Once a listener's strong pointers all disappear, the GC nulls out ("tombstones") the event's weak pointer. The next time the event occurs, it finds that it no longer has a live pointer, so it stops notifying the listener.

ephemeral :: (WeakM m, Monoid o) => o -> m o