I do find that the ergonomics of this are highly dependent on a few features of a language runtime, without which it all falls apart. Or you need language specific syntax and typically a single standard implementation.
And I can't honestly think of another paradigm that doesn't require callback functions or lambdas that, ergonomically, end up producing function implementations that end up drifting off the right side of the screen for anything more than a couple of sequential asynchronous operations.
“I can't honestly think of another paradigm that doesn't require callback functions” … Haskell, Scala, Rust all use various approaches to asynchrony that leverage these language features to provide you very usable abstractions without the “callbacks” you mention. Some of those lean on lambdas, but the scrolling off the right issue hasn’t been an issue there for at least a decade now.
Scala’s direct mode is interesting, as an example of library driven, blocking/imperative style interactions that provide most of the benefits of the monadic effect style, but in a way that’s far easier for a human to write and review.
This might not be ready for mass adoption yet, but I think it’s a sneak peek of where we’ll see some languages move to.
At most you get hidden behavior on exception propagation and end-of-life extent.
If I sat down and made a careful study of all the threading implementations we might get up to a similar number of quirks.
I would suggest though that the dimensions are generally more likely to be corner cases. Some of the dimensions mentioned in that article are fairly in-your-face for an async/await implementation and can cause serious difficulties migrating between systems fairly quickly if you make the wrong assumptions, and writing correct async/await code that isn't just straightline "await everything immediately" code has to start taking some of those things into account very quickly. The equivalent for threading is more likely to only come up rarely and in more cases the correct answer is really "don't depend on that anyhow", e.g., rather than depending on exact details of how a thread is terminated to accomplish something, just cleanly send a message with your results to whoever it is waiting for it directly and let the runtime do the cleanup without your code witnessing any effects of it. Depending on these quirks in threading code is much more likely to be bad engineering practice, rather than necessary engineering practice in the async/await case.
Also, I think that single-threaded programs, even with co-routines, are just so much nicer than multi-threaded ones. You write "index = last_index++;" and it Just Works (tm), no need to worry about locks or atomics or other thread access.
https://learn.microsoft.com/en-us/cpp/build/reference/stack-...
That's true but I'm puzzled by the decision rationale. It's undeniably a major undertaking to add first class, fine-grained processes to a language and its runtime. But time invested there gets the multiplicative upside that all language users benefit from the investment. Instead, Async/Await transfers the complexity to users of the language, as TFA describes.
As an Erlang and now gleam developer, I'm continuously grateful for the BEAM's support for fine-grained processes (note these are VM processes, not OS level). If I want to do things in parallel, I spawn a new process to do it. Do I want that concurrency because of io latency or parallel computation? Doesn't matter. Processes handle both. If I want an actor - a long(ish) lived "object" that responds to messages sent to it - I spawn it as a process. If I want to communicate between processes, I send a message. That's the only choice. No shared memory so no semaphores, locks and whatnot.
I never have to think "hmm, should this function be sync or async?" and reason about the transitive implications through the entire call stack. I write functions to calculate values. If I want function A to be called after function B in program 1, I write them sequentially. If I want to run them concurrently in program 2, I spawn them in separate processes. Concurrency is a decision at the calling site, not when writing the function being called.
One concurrency primitive that meets all the needs. The reduction in cognitive load is palpable compared to Python (the other language I use regularly).
The usual reaction is "yeah but performance". I've never found this to be an issue in real life. Sure there are benchmarks that show C/Rust/C#/whatever is faster, often meaningfully so. In practice, for my needs: never been a problem.
I'm ever more grateful for the elegance and consistency of the BEAM concurrency model. From an ergonomic perspective, Async/Await feels like a poor abstraction by comparison.
That's not to say the BEAM (or its languages) is the final word in concurrency. The strong encapsulation boundaries from Structured Concurrency[0] would be a useful addition. Though even there, Erlang's supervisor hierarchies provide a a similar mechanism. Dataflow is another interesting area (many task-concurrent design questions are essentially dataflow problems).
Even without improvement though I'd still take Erlang's approach over Async/Await every day.
More people think they need the latest and hottest in manual memory management than actually do.
More people think they need a hundred thousand threads than actually do.
If you do have one of those cases, by all means prepare for it and deal with it. But be sure you have one first. The program that exceeds so much as a 100 threads is not only exceptional, but very exceptional. The exceptions are cognitively available and leap to mind, but are nevertheless the exceptions. And, again, if you have one, deal with it, but be sure you have one.
If you're sitting there in TypeScript land writing "async" code you've already surrendered on Ultimate Efficiency anyhow. Deciding what is more efficient between a threaded program in a runtime that doesn't box everything and JIT-optimized JS code is difficult but it isn't that hard for the threaded program that isn't boxing to win out on all runtime measurements, including consumed RAM.
"You write "index = last_index++;" and it Just Works (tm), no need to worry about locks or atomics or other thread access."
That goes back to my comment about using better threading techniques. I write a lot of "index = last_index + 1" (mutably incrementing like that in a single expression is just bad style anywhere you see it) in my threaded code all the time without thinking much about it, because I use the model where by default a value belongs to the one actor process that has access to it at all. The problem isn't that mutation is dangerous in threaded code, the problem was people writing threaded code based on a ton of threads running around shared data structures with locking. Not only is that not the only way to write threaded code, it is literally the worst. There are many other options, all of them better in some way, many of them much better.
Contrasting the difficulty of writing threaded code to something else based on the assumption that "lots of shared state locked by semaphores" is the only way to write code is like a Haskell advocate talking about the amazing benefits of functional programming while writing as if literally every imperative program is just one big pile of unmitigated, pure spaghetti code where everything is linked together with gotos and every variable in the program is a global variable. That's not the relevant comparison any more. It hasn't been for a long time. If anyone's program is scrambled because they did write a big pile of gotos and global variables or they did write a big pile of shared state with semaphores everywhere, that's on them. A vast array of better techniques of all shapes and sizes was available to them.
This is fair, but is a hundred thousand OS threads really a reasonable load on reasonable OSes?
I've (helped to) run systems with millions of Erlang processes on a node, so I'm not most people and my attitudes might well be skewed. I would expect that the reasonable ceiling for OS threads is closer to 10k than 100k, although I don't think I've seen many articles about findinf the limits... maybe everything just quietly works?
If the limit is 10k, I think there's a lot of real situations where thread per connection or thread per task runs out of headroom. If the limit is 100k, a lot fewer people are going to hit that. Common wisdom is "thread per connection doesn't scale", but it has a desirable programming model, so the question becomes how to get the programming model and scale, at least to modest size.
If it's fine to mostly just use threads (hopefully without so much of the shared everything model that makes everything hard), it would be great if people knew that instead of spending so much time adding async/await to everything. :P
The problem is, the community collectively decided the problem was "threading" in general rather than "trying to have tons of threads running around shared data structures controlled via piles of simultaneously-held semaphores" specifically.
If you don't structure your threads on that basis, but instead default to something that looks more like actors and message passing, even if it isn't strictly speaking actors and message passing, the complexity comes down. Add some later elaborations like structured concurrency and a few other pre-canned design patterns for threading like a parallel map or worker pools being issued work items and it becomes merely something difficult rather than insane. When you program with threads sanely, it takes very little for async/await to actually be the substantially more complicated and difficult-to-understand choice when you have a workflow more interesting than "always await everything immediately" to implement, to say nothing of how nice it is to have things actually running on multiple cores simultaneously without having to carefully arrange for it.