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The difference is in the way that the transformations compose. Iterators are generic over some iterable type `U` in `E, R, T: Iterator<E, R, U|T>, U: Iterable<E>` that they either directly or indirectly capture some nested Iterator. However that means that code composing iterators must also be generic over that iterable `U`, so you can't simply take two transformation pipelines and concatenate them, because they will both provide an element source already. Transducers separate the transformation part and the processing part.
So you can have a `E, R, T: Transform<E, R>` which doesn't have a generic type parameter for the Iterable, and a function `transduce<E, R, S: Source<E>, I: Sink<R>, T: Transform<E, R>>(source: S, tx: T) -> I` which contains all the transformation application machinery, be it iterator style `next()` operations, or stream based `pull/push` operations,
and a function `comp<E, R, S>(left: Transform<E, R>, right: Transform<R, S>) -> Transform<E, S>` that composes transformations. The way transformers are implemented, this `comp` operation is actually simply function composition. |
Still I think just the “next()” context is quite enough because you can have everyone use it by convention for everything, even things that aren’t collections. Like a fluent tensor library for example. This is based on a quick understanding of transducers…