[n2d4]

Actually, I think this benchmark did the right thing, that I wish more benchmarks would do. I'm much less interested in what the differences between compilers are than in what the actual output will be if I ask a professional Go or Node.js dev to solve the same task. (TBF, it would've been better if the task benchmarked was something useful, eg. handling an HTTP request.)

Go heavily encourages a certain kind of programming; JavaScript heavily encourages a different kind; and the article does a great job at showing what the consequences are.

[rtpg]

But you wouldn't call a million tasks with `Promise.all` in Node, right? That's just not a thing that one does.

Instead, there's usually going to be some queue outside the VM that will leave you with _some_ sort of chunking and otherwise working in smaller, more manageable bits (that might, incidentally, be shaped in ways that the VM can handle in interesting ways).

It's definitely true to say that the "idioamatic" way of handling things is worth going into, but if part of your synthetic benchmark involves doing something quite out of the ordinary, it feels suspicious.

I generally agree that a "real" benchmark here would be nice. It would be interesting if someone could come up with the "minimum viable non-trivial business logic" that people could use for these benchmarks (perhaps coupled with automation tooling to run the benchmarks)

[hamandcheese]

> But you wouldn't call a million tasks with `Promise.all` in Node, right? That's just not a thing that one does.

But neither would you wait on a waitgroup of size 1 million in Go... right?

[ricardobeat]

You could, and the tasks would run concurrently. Node is single threaded so unless you used one of the I/O calls backed by a thread pool, they would all execute sequentially .

[rfoo]

For a goroutine doing nothing, no.

But if I have 1 million tasks which spent 10% of their time on CPU-bound codes, intermixed with other IO-bound codes, and I just want throughput and I'm too lazy to use a proper task queue, then why not?

[rtpg]

yeah, right? I mean I don't have a dog in this race, just wished we could get into "normal" repros without having to wonder if some magic is kicking in

[YetAnotherNick]

The fundamental problem is there are two kind of sleep function. One that actually sleeps and other that is a actually a timer that just calls certain callback after a desired interval. Promise is just a syntactic sugar on top of second type. Go certainly could call another function after desired interval using `Timer`.

I think better comparison would be wasting CPU for 10 seconds instead of sleep.

[jonathanstrange]

No professional Go programmer would spawn 1M goroutines unless they're sure they have the memory for it (and even then, only if benchmarks indicate it, which is unlikely). Goroutines have a static stack overhead between 2KiB to 8KiB depending on the platform. You'd use a work stealing approach with a reasonable number of goroutines instead. How many are reasonable needs to be tested because it depends on how long each Goroutine spends waiting for I/O or sleeping.

But I can go further than that: No professional programmer should run 1M concurrent tasks on an ordinary CPU no matter which language because it makes no sense if the CPU has several orders of magnitudes less cores. The tasks are not going to run in parallel anyway.

[OutOfHere]

The basis for running 1 million concurrent tasks is to support 1 million active concurrent user connections. They don't need to run in parallel if async is used. As shown, Rust and C# do well. How would you support it in Go?

[jonathanstrange]

The servers I use have limits far below 1M active connections, realistically speaking about 60k simultaneously active connections. So I can't really answer that question. However, it's easy to find answers to that question online [1]. Go is not forcing you to spawn Goroutines when you don't really need them. As I said, the correct way in Go is to use worker pools, the size of which depends on measurable performance because it is connected to how much i/o each Goroutine performs and how long it waits on average.

[1] https://www.freecodecamp.org/news/million-websockets-and-go-...

[theamk]

Pay extra $1/month for bigger server? The numbers are really small here.

[OutOfHere]

You obviously missed the chart. The memory usage difference isn't small; it's astronomical as far as scaling goes.

[lossolo]

In what world is the cost of 2.5 GB of RAM per 1 million connections an issue? Are you telling me there's a service in this world handling, for example, 100 million active connections, and they can't afford 250 GB of RAM? It's not the 90's anymore.

And we're talking about a naive microbenchmark. If you were actually building a service like that in Go (millions of active connections) and you were very concerned about memory usage, you wouldn't be naive enough to use a goroutine for every connection. Instead, you would use something like gnet or another solution based directly on epoll events, combined with a worker pool.

[yencabulator]

> If you were actually building a service like that in Go (millions of active connections) and you were very concerned about memory usage

Or, much more likely, pay a few tens of dollars per month for a second server and start scaling out.

[OutOfHere]

Go is a friendly language and it is well liked, but it is inappropriate when the absolute best performance saves more money than the additional developer salaries needed to write it in a higher performance language. An example is for deep learning or other big numerical work, where you'd be wasting expensive hardware resources if using Go. Perhaps the one million concurrent users will however be fine with Go.

[Quothling]

> Go heavily encourages a certain kind of programming;

True, but it really doesn't encourage you to run 1m goroutines with the standard memory setting. Though it's probably fair to run Go wastefully when you're comparing it to Promise.All.

[n2d4]

Of course! That's why the article is telling you that some languages (C#, Rust) are better at it than others (Go, Java). Doesn't mean that Go and Java are bad languages! Just that they aren't good to do this thing.

[Quothling]

The article is telling us that you can run really inefficient code. Goroutines should be run with worker pools and a buffered channel and it's silly to not do that and then compare it to things like an optimized Rust crate like Tokio.

[Aeolun]

Is that the ideomatic way to do it, or the best way you can imagine?

[Quothling]

> Is that the ideomatic way to do it

Well... I'm actually not sure what ideomatic means (English isn't my first language), but it's the standard way of doing it. You'll even find it as step 2 and 3 here: https://go.dev/tour/concurrency/1

> or the best way you can imagine

I would do a lot much more to tune it if you were in a position where you'd know it would run that many "tasks". I think what many non-Go programmers might run into here is that Go doesn't come with any sort of "magic". Instead it comes with a highly opinionated way of doing things. Compare that to C# which comes with a highly optimized CLR and a bunch really excellent libraries which are continuously optimized by Microsoft and you're going to end up with an article like this. The async libraries are maintaining which tasks are running (though Promise.All is obviously also binding a huge amount of memory you don't have to), while the Go example is running 1 million at once.

You'll also notice that there is no benchmark for execution time. With Go you might actually want to pay with memory, though I'd argue that you'd almost never want to run 1 million Goroutines at once.

Though to be fair to this specific author, it looks like they copied the previous benchmarks and then ran it as-is.

[guitarbill]

The post was edited, previously it just said roughly this part: "step 2 and 3 here: https://go.dev/tour/concurrency/1". Which - as far as I can tell - does not mention worker pools...

[pbhjpbhj]

Idiomatic is the word the parent was looking for. The base word is idiom.

It was probably the intent of the parent to mean 'making use of the particular features of the language that are not necessarily common to other languages'.

I'm not a programmer, but you appear to give good examples.

I hope I'm not teaching you to suck eggs... {That's an idiom, meaning teaching someone something they're already expert in. Like teaching your Grandma to suck eggs - which weirdly means blowing out the insides of a raw egg. That's done when using the egg to paint; which is a traditional Easter craft.}

[jchw]

I'm torn.

As far as practicality goes I actually agree with you: if I knew I were trying to do something to the order of 1,000,000 tasks in Go I would probably use a worker pool for this exact reason. I have done this pattern in Go. It is certainly not unidiomatic.

However, it also isn't the obvious way to do 1,000,000 things concurrently in Go. The obvious way to do 1,000,000 things concurrently in Go is to do a for loop and launch a Goroutine for each thing. It is the native unit of task. It is very tightly tied to how I/O works in Go.

If you are trying to do something like a web server, then the calculus changes a lot. In Go, due to the way I/O works, you really can't do much but have a goroutine or two per connection. However, on the other hand, the overhead that goroutines imply starts to look a lot smaller once you put real workloads on each of the millions of tasks.

This benchmark really does tell you something about the performance and overhead of the Go programming language, but it won't necessarily translate to production workloads the way that it seems like it will. In real workloads where the tasks themselves are usually a lot heavier than the constant cost per task, I actually suspect other issues with Go are likely to crop up first (especially in performance critical contexts, latency.) So realistically, it would probably be a bad idea to extrapolate from a benchmark this synthetic to try to determine anything about real world workloads.

Ultimately though, for whatever purpose a synthetic benchmark like this does serve, I think they did the correct thing. I guess I just wonder exactly what the point of it is. Like, the optimized Rust example uses around 0.12 KiB per task. That's extremely cool, but where in the real world are you going to find tasks where the actual state doesn't completely eclipse that metric? Meanwhile, Go is using around 2.64 KiB per task. 22x larger than Rust as it may be, it's still not very much. I think for most real world cases, you would struggle to find too many tasks where the working set per task is actually that small. Of course, if you do, then I'd reckon optimized async Rust will be a true barn-burner at the task, and a lot of those cases where every byte and millisecond counts, Go does often lose. There are many examples.[1]

In many cases Go is far from optimal: Channels, goroutines, the regex engine, various codec implementations in the standard library, etc. are all far from the most optimal implementation you could imagine. However, I feel like they usually do a good job making the performance very sufficient for a wide range of real world tasks. They have made some tradeoffs that a lot of us find very practical and sensible and it makes Go feel like a language you can usually depend on. I think this is especially true in a world where it was already fine when you can run huge websites on Python + Django and other stacks that are relatively much less efficient in memory and CPU usage than Go.

I'll tell you what this benchmark tells me really though: C# is seriously impressive.

[1]: https://discord.com/blog/why-discord-is-switching-from-go-to...