alt Hacker NewsIt depends on how you define the observable pattern.
The fundamental components here are laziness and weak connections between graph nodes. Node values are getting materialized only when you observe them, and the system is flexible for live structural changes.
Usually, you don't need to materialize the entire graph when you need to observe just some nodes. Additionally, you can halt computations at any point in time leaving the graph in semi-actualized state, make extra changes to the inputs, and continue materialization of the nodes of interest. The algorithm will sort out all changes for you.
Essentially, incremental computations is just a term covering these features. You can organize the same system in terms of observers and subscribers.
Perhaps, classical Excel spreadsheets is the best illustration of the idea. Also, see my article on the topic: https://medium.com/@eliah.lakhin/salsa-algorithm-explained-c...
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Laziness and weak connections makes sense as differentiators.
However I'm not sure Excel is such a great illustration in that case, as it's neither lazy nor weakly connected; at least at the surface.
> You can organize the same system in terms of observers and subscribers.
Also, the differences between "hot" and "cold" observers and the use of schedulers.
I like that about the observable pattern that while hot versus cold is confusing, it is generally "explicit" in the dance of observers/subscribers. I also tend to like the way that observable schedulers and scheduling operators are often usefully explicit in halting computations while still being largely automated in the time domain.
Certainly my gut instinct with this specific library is seeing if the stuff being done with it might be a cleaner fit in something like RxOcaml, but I realize I'm in something of a minority in preferring explicit observable operators over implicit "computation signals".