You get this for free in Haskell, and you also save on not having to remember useless terminology for something that has no application on their own outside Foldables anyways.

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Maxatar 2 hours ago | parent | next [-]

>...you also save on not having to remember useless terminology...

It may be true in this particular case, but in my admittedly brief experience using Haskell you absolutely end up having to remember a hell of a lot of useless terminology for incredibly trivial things.

	▲	tombert 2 hours ago \| parent [-]
		Terminology doesn't bother me nearly as much as people defining custom operators. I used to think it was cute the you could make custom operators in Haskell but as I've worked more with the language, I wish the community would just accept that "words" are actually a pretty useful tool.

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iLemming 2 hours ago | parent | prev | next [-]

> You get this for free in Haskell,

Oh, my favorite part of the orange site, that's why we come here, that's the 'meat of HN' - language tribalism with a technical veneer. Congratulations, not only you said something as lame as: "French doesn't need the subjunctive mood because German has word order rules that already express uncertainty", but you're also incorrect factually.

Haskell's laziness gives you fusion-like memory behavior on lists for free. But transducers solve a broader problem - portable, composable, context-independent transformations over arbitrary reducing processes - and that you don't get for free in Haskell either.

Transducers exist because Clojure is strict, has a rich collection library, and needed a composable abstraction over reducing processes that works uniformly across collections, channels, streams, and anything else that can be expressed as a step function. They're a solution to a specific problem in a specific context.

Haskell's laziness exists because the language chose non-strict semantics as a foundational design decision, with entirely different consequences - both positive (fusion, elegant expression of infinite structures) and negative (space leaks, reasoning difficulty about resource usage).

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instig007 an hour ago | parent [-]

> Haskell's laziness gives you fusion-like memory behavior on lists for free.

Haskell laziness & fusion isn't limited to lists, you can fuse any lawful composition of functions applied over data with the required lawful instances used for the said composition. There's no difference to what transducers are designed for.

> But transducers solve a broader problem - portable, composable, context-independent transformations over arbitrary reducing processes - and that you don't get for free in Haskell either.

Transducers don't solve a broader problem, it's the same problem of reducing complexities of your algorithims by eliminating transient data representations. If you think otherwise, I invite you to provide a practical example of the broader scope, especially the part about "context-independent transformations" that would be different to what Haskell provides you without that separate notion.

> and negative (space leaks, reasoning difficulty about resource usage).

which is mostly FUD spread by internet crowd who don't know the basics of call-by-need semantics, such as the places you don't bind your intermediate evaluations at, and what language constructs implicitly force evaluations for you.

	▲	iLemming 37 minutes ago \| parent [-]
		> you can fuse any lawful composition of functions each of those requires manually written rewrite rules or specific library support. It's not a universal property that falls out of laziness - it's careful engineering per data type. Transducers work over any reducing function by construction, not by optimization rules that may or may not fire. > it's the same problem It is not. Take a transducer like `(comp (filter odd?) (map inc) (take 5))`. You can apply this to a vector, a lazy seq, a core.async channel, or a custom step function you wrote five minutes ago. The transformation is defined once, independent of source and destination. In Haskell, fusing over a list is one thing. Applying that same composed transformation to a conduit, a streaming pipeline, an io-streams source, and a pure fold requires different code or different typeclass machinery for each. You can absolutely build this abstraction in Haskell (the foldl library gets close), but it's not free - it's a library with design choices, just like transducers are. You're third claim is basically the "skill issue" defense. Two Haskell Simons - Marlow, and Jones, and also Edward Kmett have all written and spoken about the difficulty of reasoning about space behavior in lazy Haskell. If the people who build the compiler and its core libraries acknowledge it as a real trade-off, dismissing it as FUD from people who "don't know the basics" is not an argument. It's gatekeeping. Come on, how can you fail to see the difference between: "Haskell can express similar things" with "Haskell gives you this for free"?

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eduction 2 hours ago | parent | prev [-]

It goes beyond a foldable, can be applied to streams. Clojure had foldables, called reducers, this was generalized further when core.async came along - transducers can be attached to core async channels and also used in places where reducers were used. The terminology is used to document the thing that various contexts accept (chan, into, sequence, eduction etc). They exist to make the language simpler and more general. They could actually allow a bunch of old constructs to be dispensed with but came along too late to build the whole language around.

	▲	instig007 an hour ago \| parent [-]
		> It goes beyond a foldable, can be applied to streams. > Clojure had foldables, called reducers, this was generalized further when core.async came along - transducers can be attached to core async channels and also used in places where reducers were used. Ok, you mean there's a distinction between foldables and the effectful and/or infinite streams, so there's natural divide between them in terms of interfaces such as (for instance) `Foldable f` and `Stream f e` where `e` is the effect context. It's a fair distinction, however, I guess my overall point is that they all have applicability within the same kind of folding algorithms that don't need a separate notion of "a composing object that's called a transducer" if you hop your Clojure practice onto Haskell runtime where transformations are lazy by default.