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1 Abstract

Reactive is a simple foundation for programming reactive systems functionally. Like Fran/FRP, it has a notions of (reactive) behaviors and events. Like DataDriven, Reactive has a data-driven implementation. The main difference between Reactive and DataDriven are

  • Reactive builds on STM-based IVars, while DataDriven builds on continuation-based computations; and
  • The algebra of events and reactive values (called events and sources in DataDriven) are purely functional. I couldn't figure out how to accomplish that in DataDriven.
  • Reactive manages (I hope) to get the efficiency of data-driven computation with a (sort-of) demand-driven architecture. For that reason, Reactive is garbage-collector-friendly, while DataDriven depends on weak references (because GC favors demand-driven computation.)
The inspiration for Reactive was Mike Sperber's [Lula] implementation of FRP. Mike used blocking threads, which I had never considered for FRP. While playing with the idea, I realized that I could give a very elegant and efficient solution to caching, which DataDriven doesn't do. (For an application
f <*> a
of a varying function to a varying argument, caching remembers the latest function to apply to a new argument and the last argument to which to apply a new function.)

As with DataDriven, Reactive provides instances for Monoid, Functor, Applicative, and Monad.

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Please leave comments at the Talk page.

2 IVars

3 Events and reactive values

4 Constant-optimized functions