Hi Tom,
I'll have an attempt at addressing the questions, although I freely admit that I'm not as "into" Reactive as Conal is yet, so he may come and correct me in a minute. [...] Reactive has explicitly parameterized inputs. In your robot example I > would expect something along the lines of
data RobotInputs = RI {lightSensor :: Behavior Colour; bumbSwitch :: Event ()} -- A couple of example robot sensors
robotBehavior :: RobotInputs -> Behavior Robot robotBehavior sensors = a behovior that combines the light sensor and the bumb switch to stay in the light, but not keep driving into things.
This looks exactly like Classical FRP. And if it is like Classical FRP behind the scenes, it nicely exemplifies the problem. In Classical FRP, Behavior is actually what I would call a signal function. When started (switched into), they map the system input signal from that point in time to a signal of some particular type. So, the record RobotInputs is just a record of lifted projection functions that selects some particular parts of the overall system input. Behind the scenes, all Behaviors are connected to the one and only system input.
data UIInputs = UI {mousePoint :: Behavior Point; mouseClick :: Event (); ...}
world :: UIInputs -> Behavior World world = interpret mouse and produce a world with barriers, robots and lights in it
Fine, of course, assuming that all behaviours share the same kind of system input, in this case UI input. But what if I want my reactive library to interface to different kinds of systems? The robot code should clearly work regardless of whether we are running it on a real hardware platform, or in a simulated setting where the system input comes form the GUI. In Classical FRP, this was not easily possible, because all combinators at some level need to refer to some particular system input type which is hardwired into the definitions. Had Haskell had ML-style parameterized modules, that would likely have offered a solution: the libraries could have been parameterized on the system input, and then one could obtain say robot code for running on real hardware or in a simulated setting by simply applying the robot module to the right kind of system input. An alternative is to parameterize the behaviour type explicitly on the system input type: Behavior sysinput a This design eventually evolved into Arrowized FRP and Yampa. So, from your examples, it is not clear to what extent Reactive as addressed this point. Just writing functions that maps behaviours to behaviours does not say very much. On a more philosophical note, I always found it a bit odd that if I wanted to write a function that mapped a signal of, say, type "a", which we can think of as type Signal a = Time -> a to another signal, of type "b" say, in Classical FRP, I'd have to write a function of type Behavior a -> Behavior b which really is a function of type (Signal SystemInput -> Signal a) -> (Signal SystemInput -> Signal b) I find this unsatisfying, as my mapping from a signal of type a to a signal of type b is completely independent from the system input (or the function wouldn't have a polymorphic type).
* A clear separation between signals, signal functions, and ordinary functions and values, yet the ability to easily integrate all kinds of computations.
I agree and disagree here (that'll be the matter of taste creeping in). I agree that in Reactive you often spend a lot of keystrokes lifting pure values into either an Event or a Behavior. Having said that I'd argue that Yampa requires us to do this too -- it merely enforces the style in which we do it (we must do it with arrows).
Yes, there is lifting in Yampa, but the arrow syntax mostly does it for the programmer, which in practice (in my experience) translates to a lot less effort, and, in my opinion, leads to clearer code as it is easy to maintain a distinction between signals and static values. After all, why should I want to live a constant to a signal, if all I'm going to do with it is to apply one and the same function to it over and over? (I'm not worried about efficiency here, that can be fixed: it's a philosophical point.) Also, form practical experience when programming with Classical FRP, we often lifted entire libraries we wanted to use to avoid having to write explicit lifts all the time. Tedious, but OK, doable. However, quite often we then discovered that actually, we needed the unlifted version of the library too, leading to name clashes and thus extra noise to do the need to disambiguate, be it by qualified input or naming the lifted versions differently. Not a show stopper by any means, but a tedious extra level of concerns. The arrow framework offer clear guidance in this case which translates to convenient coding practice: just use whatever library you need and let the arrow syntax take care of liftings where necessary.
My personal opinion on this one is that I prefer the applicative interface to the arrow based one, because it feels more like just writing a functional program.
It is true that the arrow syntax sometimes is a bit too "linear". For example, if (without using the basic arrow combinators), I want to apply first one function sf1 and then another sf2 to some signal x, one might write y <- sf1 -< x z <- sf1 -< y whereas z = sf1 (sf2 x) would arguably be clearer in a case like this. On balance, though, I find that the advantages of the arrow framework outweighs such inconveniences. (Of course, one can also write z <- sf2 <<< sf1 -< x And I think the arrow syntax likely could be tweaked to allow something more similar to the second version, but that's of course beside the point.)
I reserve judgement at the moment because I haven't explicitly written a reactive program involving a collection of behaviors, having said that, I see no reason why removing a value from the list in a Behavior > [a], it should not get garbage collected.
* There was also an issue with Classical FRP having to do with the need to observe the output from one part of the system in another part of the system.
My understanding is that Conal went to great lengths to make sure
But just the possibility of have list output is not sufficient. What is needed is a way to run a collection of independent behaviours in parallel. Classical FRP provided essentially the following functionality for this purpose: [Behavior a] -> Behavior [a] This is fine, until we get to a point where we want to remove one of those behaviors without disturbing the others. In Classical FRP, the only thing that could be done was to apply a filter to the output list to *hide* the output(s) from some of the behaviours from the outside world. But this only makes it *look* as if they're gone. In fact, they're still there, consuming computational resources, and can be resurrected at any point. Yampa provides a way of maintaining dynamic collections of signal functions, allowing new signal functions to be started and others to be removed without affecting the other signal functions in the collection. It is still unclear to me if Reactive offers anything similar. In principle, just looking from the outside, I cannot see why reactive couldn't do something similar to Yampa or possibly adopt some other design to the same effect. But my understanding is that Reactive has a fairly elaborate run-time machinery behind the scenes, and I don't know if that would get in the way or not. that > Behaviors get correctly cached, so that incremental values are only
evaluated once, but I'm affraid I can't answer this more sensibly.
This is a semantical issue, not one about efficiency. The question is this. Suppose we define let n :: Behavior Int n = <behaviour that counts left mouse button clicks> in n `until` <some event> -=> n I'm not sure I got the syntax right. But the idea is that we output the number of left mouse button clicks, and then at some point, we switch to a behavior that again output the number of left mouse button clicks, notionally the "same" one "n". The question is, after the switch, do we observe a count that continues from where the old one left off, i.e. is there a single *shared* instance of "n" that is merely being *observed* from within the two "branches" of the switch, or is the counting behavior "n" restarted (from 0) after the switch? In Classical FRP, the answer is the latter (because "n" really is a signal function mapping system input to an signal carrying integer counts). Sometimes that's what one wants, other times not. Which is what motivated the "running in" design to effectively allow a single instance of a behavior to be created, whose output then could be observed within the scope of the definition thus avoiding restarting the behavior after each switch. But this design became very complicated, and also somewhat confusing as there was no type distinction between "running behaviors" (effectively signals) and behaviors (signal functions). Yampa, by effectively providing both signals and signal functions, provides a much simpler answer, albeit at the price of a bit of extra "plumbing" sometimes. Again, I don't know where Reactive stands on this. But it is a real concern in terms of being able to express what one need to express in real-life reactive programming, i.e. more than just a matter of taste in this case.
Your email triggered to think about a couple of the other significant differences between Yampa and Reactive * Reactive deals with continuous functions of time, not sampled ones. This allows for asynchronous sampling, for example the ability to sample a Behavior at 1/60th of a second rate for screen refreshes, while sampling the same behavior also at 1/10th second for logging, and 1/1000th for euler integration of un-integratable Behaviors.
I'm not quite sure what you're getting at here. On the one hand, Yampa also notionally has continuous time. On the other hand, ANY implementation will have to do sampling at some point. But I suppose what you're saying is that Reactive allows different part of a system to be sampled at different rates in a transparent manner? Which is nice. But the tradeoffs are not clear to me.
* Reactive is push based. The result of this is that we will not waste CPU cycles for example refreshing the screen when nothing has changed on the output.
The optimizations of Yampa also achieves a fair amount of "pushing". But granted, Yampa is fundamentally pull-based. That said, for truly hybrid systems, that do a lot of continuous computation, it is not at all clear that push is a clear winner. Only extensive benchmarking can really provide genuine insight into the actual pros and cons here of different FRP implementations for various applications, I'd say. Also, when there is a need to combine output from different parts of a system, and this is done by combining the various outputs into a tuple or record, then one have to push combined output whenever any one of the constituent parts changes, which means one lose track of the changes down the line, possibly resulting in redundant computations anyway. Best, /Henrik -- Henrik Nilsson School of Computer Science The University of Nottingham nhn@cs.nott.ac.uk This message has been checked for viruses but the contents of an attachment may still contain software viruses, which could damage your computer system: you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation.