> I'm not here to disparage the work that came before — I'm here to start a conversation about what can potentially come next.

Terrible LLM-slop style. Is Mr Snell letting an LLM write the article for him or has he just appropriated the style?

They propose just using an async iterator of UInt8Array. I almost like this idea, but it's not quite all the way there.

They propose this:

  type Stream<T> = {
    next(): Promise<{ done, value: UInt8Array<T> }>
  }

  type Stream<T> = {
    next(): { done, value: T } | Promise<{ done, value: T }>
  }

- I can easily make mine from theirs

- In theirs the conceptual "stream" is defined by an iterator of iterators, meaning you need a for loop of for loops to step through it. In mine it's just one iterator and it can be consumed with one for loop.

- I'm not limited to having only streams of integers, they are

- My way, if I define a sync transform over a sync input, the whole iteration can be sync making it possible to get and use the result in sync functions. This is huge as otherwise you have to write all the code twice: once with sync iterator and for loops and once with async iterators and for await loops.

- The problem with thrashing Promises when splitting input up into words goes away. With async iterators, creating two words means creating two promises. With stream iterators if you have the data available there's no need for promises at all, you just yield it.

- Stream iterators can help you manage concurrency, which is a huge thing that async iterators cannot do. Async iterators can't do this because if they see a promise they will always wait for it. That's the same as saying "if there is any concurrency, it will always be eliminated."

  import { Repeater } from "@repeaterjs/repeater";
  
  const keys = new Repeater(async (push, stop) => {
    const listener = (ev) => {
      if (ev.key === "Escape") {
        stop();
      } else {
        push(ev.key);
      }
    };
    window.addEventListener("keyup", listener);
    await stop;
    window.removeEventListener("keyup", listener);
  });
  const konami = ["ArrowUp", "ArrowUp", "ArrowDown", "ArrowDown", "ArrowLeft", "ArrowRight", "ArrowLeft", "ArrowRight", "b", "a"];
  (async function() {
    let i = 0;
    for await (const key of keys) {
      if (key === konami[i]) {
        i++;
      } else {
        i = 0;
      }
      if (i >= konami.length) {
        console.log("KONAMI!!!");
        break; // removes the keyup listener
      }
    }
  })();

It’s one of those abstractions that’s feature complete and stable, and looking at NPM it’s apparently getting 6.5mil+ downloads a week for some reason.

Lately I’ve just taken the opposite view of the author, which is that we should just use streams, especially with how embedded they are in the `fetch` proposals and whatever. But the tee critique is devastating, so maybe the author is right. It’s exciting to see people are still thinking about this. I do think async iterables as the default abstraction is the way to go.

I'm working on a db driver that uses it by convention as part of connection/pool usage cleanup.

https://github.com/tc39/proposal-observable

If you're dealing with small objects at the production side, like individual tag names, attributes, bindings, etc. during SSR., the natural thing to do is to just write() each string. But then you see that performance is terrible compared to sync iterables, and you face a choice:

  1. Buffer to produce larger chunks and less stack switching. This is the exact same thing you need to do with Streams. or

  2. Use sync iterables and forgo being able to support async components.

We faced this problem in Lit-SSR and our solution was to move to sync iterables that can contain thunks. If the producer needs to do something async it sends a thunk, and if the consumer receives a thunk it must call and await the thunk before getting the next value. If the consumer doesn't even support async values (like in a sync renderToString() context) then it can throw if it receives one.

This produced a 12-18x speedup in SSR benchmarks over components extracted from a real-world website.

I don't think a Streams API could adopt such a fragile contract (ie, you call next() too soon it will break), but having some kind of way where a consumer can pull as many values as possible in one microtask and then await only if an async value is encountered would be really valuable, IMO. Something like `write()` and `writeAsync()`.

The sad thing here is that generators are really the right shape for a lot of these streaming APIs that work over tree-like data, but generators are far too slow.

The async iterable approach makes so much more sense because it composes naturally with for-await-of and plays well with the rest of the async/await ecosystem. The current Web Streams API has this weird impedance mismatch where you end up wrapping everything in transform streams just to apply a simple operation.

Node's original stream implementation had problems too, but at least `.pipe()` was intuitive. You could chain operations and reason about backpressure without reading a spec. The Web Streams spec feels like it was written by the kind of person who thinks the solution to a complex problem is always more abstraction.

[1] https://github.com/square/okio

[2] https://www.youtube.com/watch?v=Du7YXPAV1M8

At a native level (C++/rust), a Promise is just a closure added to a list of callbacks for the event loop. Yes, if you did 1 per streamed byte then it would be huge but if you're doing 1 promise per megabyte, (1000 per gig), it really shouldn't add up 1% of perf.

the idea is basically just use functions. no classes and very little statefulness

https://www.npmjs.com/package/pull-stream

That's an inherent flaw of garbage collected languages. Requiring to explicitly close a resource feels like writing C. Otherwise you have a memory leak or resource exhaustion, because the garbage collector may or may not free the resource. Even C++ is better at this, because it does reference counting instead.

Ideally splitting out the use cases would allow both implementations to be simpler, but that ship has probably sailed.

I tried several implementations, tweaked settings, but ultimately couldn't get around it. In some cases I had bizarre drops in activity when the consumer was below capacity.

It could have been related to the other issue they mention, which is the cost of using promises. My streams were initiating HEAPS of promises. The cost is immense when you're operating on a ton of data.

Eventually I had to implement some complex logic to accomplish batching to reduce the number of promises, then figure out some clever concurrency strategies to manage backpressure more manually. It worked well.

Once I was happy with what I had, I ported it from Deno to Go and the result was so stunningly different. The performance improvement was several orders of magnitude.

I also built my custom/native solution using the Effect library, and although some people claim it's inefficient and slow, it out-performed mine by something like 15% off the shelf, with no fine-tuning or clever ideas. I wished I'd used it from the start.

The difference is likely in that it uses a fiber-based model rather than promises at the execution layer, but I'm not sure.

Here's the WritableConsumableStream module:

https://github.com/SocketCluster/writable-consumable-stream

SocketCluster solves the problem of maintaining message order with async processing.

This feature is even more useful now with LLMs as you can process data live, transform streams with AI with no risk of mangling the message order.

I may have been the first person to use a for-await-of loop in this way with backpressure. At least on an open source project.

    const buffer = new UInt8Array(256)
    const bytesRead = await reader.read(buffer)
    if (bytesRead === 0) {
      // Done
      return
    }

We have run into many problems with web streams over the years and solving them has always proven to be hairy, including the unbounded memory growth from response.clone().

The Deno team implemented a stream API inspired by Go, which I was happy with, until they ultimately acquiesced to web streams.

This proposal shares some of those principles as well.

I’ve read my fair share of LLM slop. This doesn’t qualify.

> - I can easily make mine from theirs

That... doesn't make it superior? On the contrary, theirs can't be easily made out of yours, except by either returning trivial 1-byte chunks, or by arbitrary buffering. So their proposal is a superior primitive.

On the whole, I/O-oriented iterators probably should return chunks of T, otherwise you get buffer bloat for free. The readv/writev were introduced for a reason, you know.

Adding types on top of that isn't a protocol concern but an application-level one.

From what it looks like, they want their streams to be compatible with AsyncIterator so it'd fit into existing ecosystem of iterators.

And I believe the Uint8Array is there for matching OS streams as they tend to move batches of bytes without having knowledge about the data inside. It's probably not intended as an entirely new concept of a stream, but something that C/C++ or other language that can provide functionality for JS, can do underneath.

For example my personal pet project of a graph database written in C has observers/observables that are similar to the AsyncIterator streams (except one observable can be listened to by more than one observer) moving about batches of Uint8Array (or rather uint8_t* buffer with capacity/count), because it's one of the fastest and easiest thing to do in C.

It'd be a lot more work to use anything other than uint8_t* batches for streaming data. What I mean by that, is that any other protocol that is aware of the type information would be built on top of the streams, rather than being part of the stream protocol itself for this reason.

But Observables really do not solve the problems being talked about in this post.

[1] https://github.com/WICG/observable [2] https://github.com/WICG/observable/issues/216

  type Stream<T> = {
    next(): { done, value: T } | Promise<{ done, value: T }>
  }

Engineers had a collective freak out panic back in 2013 over Do not unleash Zalgo, a worry about using callbacks with different activation patterns. Theres wisdom there, for callbacks especially; it's confusing if sometime the callback fires right away, sometimes is in fact async. https://blog.izs.me/2013/08/designing-apis-for-asynchrony/

And this sort of narrow specific control has been with us since. It's generally not cool to use MaybeAsync<T> = T | Promise<T>, for similar "it's better to be uniform" reasons. We've been so afraid of Zalgo for so long now.

That fear just seems so overblown and it feels like it hurts us so much that we can't do nice fast things. And go async when we need to.

Regarding the pulling multiple, it really depends doesn't it? It wouldn't be hard to make a utility function that lets you pull as many as you want queueing deferrables, allowing one at a time to flow. But I suspect at least some stream sources would be just fine yielding multiple results without waiting. They can internally wait for the previous promise, use that as a cursor.

I wasn't aware that generators were far too slow. It feels like we are using the main bit of the generator interface here, which is good enough.

Also I'm curious why you say that generators are far too slow. Were you using async generators perhaps? Here's what I cooked up using sync generators: https://github.com/bablr-lang/stream-iterator/blob/trunk/lib...

This is the magic bit:

  return step.value.then((value) => {
    return this.next(value);
  });

If you go back a few versions, that number goes up to around 105x. I don’t recall now if I tested back to 14. There was an optimization to async handling in 16 that I recall breaking a few tests that depended on nextTick() behavior that stopped happening, such that the setup and execution steps started firing in the wrong order, due to a mock returning a number instead of a Promise.

I wonder if I still have that code somewhere…

So if you're going to flatten everything into one stream then you can't have a for loop implementation that defensively awaits on every step, or else it'll be slooooooooow. That's my proposal for the change to the language is a syntax like

  for await? (value of stream) {
  }

edit: I found where stop is created[1]. I can't say I've seen this pattern before, and the traditionalist in me wants to dislike the API for contradicting conventions, but I'm wondering if this was designed carefully for ergonomic benefits that outweigh the cost of violating conventions. Or if this was just toy code to try out new patterns, which is totally legit also

[1]: https://github.com/repeaterjs/repeater/blob/638a53f2729f5197...