Monadic RDP

Reactive Demand Programming (RDP) is a programming model I developed circa 2010-2012. I’ve tabled it for a few years to work on other project goals, and since then I’ve also become interested in Kahn Process Networks (KPNs) as a simpler alternative in some ways. But RDP still is an interesting paradigm, with features convenient for live programming and open, distributed systems.

The essential idea for RDP is simply this: concurrent effects should be commutative and idempotent.

That is, expressing a specific request twice is the same as expressing it one or three times. If we have two different requests at the same logical time, the order in which we evaluate or observe them should not matter. With these properties, we preserve a lot of the nice equational reasoning and refactoring features associated with purely functional programming, and a lot of declarative reasoning is feasible. Essentially, effects are sets of declarations for input or output.

There are also some secondary ideas developed for RDP, such as precise logical timing. You can search this blog for more on the subject.

Anyhow, the original RDP models are arrowized, but monadic expression of behaviors would likely be more convenient.

Instead of normal imperative variables, which have one value at a time, our monadic RDP system will only have set variables, each with a label. Writing a variable simply adds a given value to the set. Reading the variable will return the prior value at the label, which will be a set.

The sets observed upon reading a variable must have deterministic ordering and duplicates removed, so we only support comparable value types. Actually, rather than sets we could use any commutative, idempotent monoid type. But sets are the most obvious such type.

Like RDP arrowized behaviors, our monad value would be applied continuously, corresponding to a stateless behavior applied in just one time-step. Like arrowized RDP, we could also support a “delay” operation that enables RDP behaviors to be distributed over multiple time-steps, but we might need to constrain the number of delay steps (otherwise we end up with untethered, fire-and-forget RDP behaviors – which is interesting, but undermines any live programming features). Effects would be modeled in terms exposing a subset of variables for external input or output. We might also add a few auxiliary features, such as single-assignment (per time step) labels, value suppression, forking of behavior threads, hierarchical structure for labels, and so on.

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This entry was posted in Concurrency, Distributed Programming, Language Design, Live Programming, Open Systems Programming, State. Bookmark the permalink.

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