I feel this benchmark compares apples to oranges in some cases.

For example, for node, the author puts a million promises into the runtime event loop and uses `Promise.all` to wait for them all.

This is very different from, say, the Go version where the author creates a million goroutines and puts `waitgroup.Done` as a defer call.

While this might be the idiomatic way of concurrency in the respective languages, it does not account for how goroutines are fundamentally different from promises, and how the runtime does things differently. For JS, there's a single event loop. Counting the JS execution threads, the event loop thread and whatever else the runtime uses for async I/O, the execution model is fundamentally different from Go. Go (if not using `GOMAXPROCS`) spawns an OS thread for every physical thread that your machine has, and then uses a userspace scheduler to distribute goroutines to those threads. It may spawn more OS threads to account for OS threads sleeping on syscalls. Although I don't think the runtime will spawn extra threads in this case.

It also depends on what the "concurrent tasks" (I know, concurrency != parallelism) are. Tasks such as reading a file or doing a network call are better done with something like promises, but CPU-bound tasks are better done with goroutines or Node worker_threads. It would be interesting to see how the memory usage changes when doing async I/O vs CPU-bound tasks concurrently in different languages.

There's a difference between "running a task that waits for 10 seconds" and "scheduling a wakeup in 10 seconds".

The code for several of the languages that are low-memory usage that do the second while the high memory usage results do the first. For example, on my machine the article's go code uses 2.5GB of memory but the following code uses only 124MB. That difference is in-line with the rust results.

  package main
  
  import (
    "os"
    "strconv"
    "sync"
    "time"
  )
  
  func main() {
    numRoutines, _ := strconv.Atoi(os.Args[1])
    var wg sync.WaitGroup
    for i := 0; i < numRoutines; i++ {
      wg.Add(1)
      time.AfterFunc(10*time.Second, wg.Done)
    }
    wg.Wait()
  }

I don't know what's a fair way to do this for all languages listed in the benchmark, but for Go vs Node the only fair way would be to use a single goroutine to schedule timers and another one to pick them up when they tick, this way we don't create a huge stack and it's much more comparable to what you're really doing in Node.

Consider the following code:

package main

import ( "fmt" "os" "strconv" "time" )

func main() {

    numTimers, _ := strconv.Atoi(os.Args[1])

    timerChan := make(chan struct{})

    // Goroutine 1: Schedule timers
    go func() {
        for i := 0; i < numTimers; i++ {
            timer := time.NewTimer(10 * time.Second)
            go func(t *time.Timer) {
                <-t.C
                timerChan <- struct{}{}
            }(timer)
        }
    }()

    // Goroutine 2: Receive and process timer signals
    for i := 0; i < numTimers; i++ {
        <-timerChan
    }

Also for Node it's weird not to have Bun and Deno included. I suppose you can have other runtimes for other languages too.

In the end I think this benchmark is comparing different things and not really useful for anything...

> high number of concurrent tasks can consume a significant amount of memory

note absolute numbers here: in the worst case, 1M tasks consumed 2.7 GB of RAM, with ~2700 bytes overhead per task. That'd still fit in the cheapest server with room to spare.

My conclusion would be opposite: as long as per-task data is more than a few KB, the memory overhead of task scheduler is negligible.

This depends a lot on how you define "concurrent tasks", but the article provides a definition:

Let's launch N concurrent tasks, where each task waits for 10 seconds and then the program exists after all tasks finish. The number of tasks is controlled by the command line argument.

Leaving aside semantics like "since the tasks aren't specified as doing anything with side effects, the compiler can remove them as dead code", all you really need here is a timer and a continuation for each "task" -- i.e 24 bytes on most platforms. Allowing for allocation overhead and a data structure to manage all the timers efficiently, you might use as much as double that; with some tricks (e.g. function pointer compression) you could get it down to half that.

Eyeballing the graph, it looks like the winner is around 200MB for 1M concurrent tasks, so about 4x worse than a reasonably efficient but not heavily optimized implementation would be.

I have no idea what Go is doing to get 2500 bytes per task.

> Now Go loses by over 13 times to the winner. It also loses by over 2 times to Java, which contradicts the general perception of the JVM being a memory hog and Go being lightweight.

Well, if it isn't the classic unwavering confidence that an artificial "hello world"-like benchmark is in any way representative of real world programs.

While it’s nice to compare languages with simple idiomatic code I think it’s unfair to developers to show them the performance of an entirely empty function body and graphs with bars that focus on only one variable. It paints a picture that you can safely pick language X because it had the smaller bar.

I urge anyone making decisions from looking at these graphs to run this benchmark themselves and add two things:

- Add at least the most minimal real world task inside of these function bodies to get a better feel for how the languages use memory

- Measure the duration in addition to the memory to get a feel for the difference in scheduling between the languages

I write (async) Rust regularly, and I don't understand how the version in the appendix doesn't take 10x1,000,000 seconds to complete. In other words, I'd have expected no concurrency to take place.

Am I wrong?

UPDATE: From the replies below, it looks like I was right about "no concurrency takes place", but I was wrong about how long it takes, because `tokio::time::sleep()` keeps track of when the future was created, (ie when `sleep()` was called) instead of when the future is first `.await`ed (which was my unsaid assumption).

Regarding Java I'm pretty sure that benchmark is broken at least a little bit and testing something else as not specifying initial size for ArrayList means list of size 10 which gets resized all the time when `add()` is called, leading to big amount of unused objects needing garbage collection.

I’m still baffled that some people are bold enough to voluntarily posts those kind of most-of-the-time useless “benchmark” that will inevitably be riddled with errors. I don’t know what pushes them. In the end you look like a clown more often than not.

Good to see NativeAOT getting positive press.

Go won because it served a need felt by many programmers: a garbage-collected language which compiled to native code, with robust libraries supported by a large corp.

With Native AOT, C# is walking into the same space. With arguably better library selection, equivalent performance, and native code compilation. And a much more powerful, well-thought-out language - at a slight complexity cost. If you're starting a project today (with the luxury of choosing a language), you should give C# + NativeAOT a consideration.

Did a similar benchmark in Kotlin using co-routines.

    import kotlin.time.Duration.Companion.milliseconds
    import kotlin.time.measureTime
    import kotlinx.coroutines.async
    import kotlinx.coroutines.awaitAll
    import kotlinx.coroutines.coroutineScope
    import kotlinx.coroutines.delay
    
    suspend fun main() {
        measureTime {
            coroutineScope {
                (0..1000000).map {
                    async {
                        delay(1.milliseconds)
                    }
                }.awaitAll()
            }
        }.let { t ->
            println("Took $t")
            val runtime = Runtime.getRuntime()
    
            val maxHeapSize = runtime.maxMemory() 
            val allocatedHeapSize = runtime.totalMemory()
            val freeHeapSize = runtime.freeMemory()
    
            println("Max Heap: ${maxHeapSize / 1024 / 1024} MB")
            println("Allocated Heap: ${allocatedHeapSize / 1024 / 1024} MB")
            println("Free Heap: ${freeHeapSize / 1024 / 1024} MB")
        }
    }

   Took 1.597011084s
   Max Heap: 4096 MB
   Allocated Heap: 2238 MB
   Free Heap: 1548 MB

It would be nice if the author also compared different runtimes (e.g. NodeJS vs Deno, or cpython vs pypy) and core language engines (e.g. v8 vs spider monkey vs JavaScript core)

Where is erlang? Sleeping is not running, by the way. If you just sleep, in Erlang you would use a hibernated process.

I feel this is so misleading. For example, by default after spawning, Erlang would have some memory preallocated for each process, so they don't need to ask the operation system for new allocations (and if you want to shrink it, you call hibernate).

Do something more real, like message passing with one million processes or websockets. Or 1M tcp connections. Because, the moment you send messages, here is when the magic happens (and memory would grow, the delay when each message is processed would be different in different languages).

Oh, and btw, if you want to do THAT in erlang, use timer:apply_after(Time, Module, Function, Arguments). Which would not spawn an erlang process, just would put the task to the timer scheduling table.

And Elixir was in the old article, and they implemented it all wrong. Sad.

Maybe I’m missing something here but surely Node isn’t doing anything concurrently? Promises don’t execute concurrently, they just tidy up async execution. The code as given will just sequentially resolve a million promises. No wonder it looks so good. You’d need to be using workers to actually do anything concurrently.

It would be more interesting to see a benchmark where a task will not be empty but would have an open network connection e.g. would make an HTTP request to a test server with 10 seconds response time. Network is a frequent reason real world applications spawn 1M tasks.

Out of curiosity, I checked if using uvloop[0] in Python changed the numbers.

This is the code:

  # /// script
  # requires-python = ">=3.12"
  # dependencies = ["uvloop"]
  # ///
  
  import asyncio
  import sys
  
  import uvloop
  
  
  async def main(num_tasks):
      tasks = []
  
      for task_id in range(num_tasks):
          tasks.append(asyncio.sleep(10))
  
      await asyncio.gather(*tasks)
  
  
  if __name__ == "__main__":
      num_tasks = int(sys.argv[1])
      # uvloop.run(main(num_tasks))
      asyncio.run(main(num_tasks))

  /usr/bin/time -l -p -h uv run async-memory.py 100000

  170835968  maximum resident set size

  204259328  maximum resident set size

[0]: https://github.com/MagicStack/uvloop/

Just tried this in Julia: 16.0 GB of memory for 1M tasks!

I believe each task in Julia has its own stack, so this makes sense. Still, it does mean you've got to take account of ~16 KB of memory per running task which is not great.

RUST

The rust code is really checking how big Tokio's structures that track timers are. Solving the problem in a fully degenerate manner, the following code runs correct correctly and uses only 35MB peak. 35 bytes per future seems pretty small. 1 billion futures was ~14GB and ran fine.

    #[tokio::main]
    async fn main() {
        let sleep = SleepUntil {
            end: Instant::now() + Duration::from_secs(10),
        };
        let timers: Vec<_> = iter::repeat_n(sleep, 1_000_000_0).collect();
        for sleep in timers {
            sleep.await;
        }
    }

    #[derive(Clone)]
    struct SleepUntil {
        end: Instant,
    }

    impl Future for SleepUntil {
        type Output = ();

        fn poll(self: Pin<&mut Self>, cx: &mut Context) -> Poll<Self::Output> {
            if Instant::now() >= self.end {
                Poll::Ready(())
            } else {
                cx.waker().wake_by_ref();
                Poll::Pending
            }
        }
    }

The point I'm making here is that synthetic benchmarks often measure something which doesn't help much. While the above is really degenerate, it shares the same problems as the article's code (it just leans into problems much harder).

Why C with pthreads missing in this benchmark ?

Can someone explain the node version to me? My js knowledge is from a decade ago. AFAIK, setTimeout creates a timer and returns a handle to it. What does promisify do? I'd assume it's a general wrapper that takes a function that returns X and wraps it so that it returns Promise<X>. So that code actually runs 10k tasks that each create a timer with a timeout of 10 seconds and return immediately.

Reminder that Rust does not automatically schedule anything. Unless you _explicitly_ call `tokio::spawn` or `async_std::spawn` you are still living entirely in state-machine land.

Rust's `join_all` uses `FuturesUnordered` behind the scenes, which is pretty intelligent in terms of keeping track of which tasks are ready to make progress, but it does not use tokio/async_std for scheduling. AFAICT the only thing being measured about tokio/async_std is the heap size of their `sleep` implementations.

I'd be very interesting in seeing how Tokio's actual scheduler performs. The two ways to do that are:

- using https://docs.rs/tokio/latest/tokio/task/join_set/struct.Join... to spawn all the futures and then await them

- spawn each future in the global scheduler, and then await the JoinHandles using the for loop from the appendix

As other commenters have noted, calling `sleep` only constructs a state machine. So the Appendix isn't actually concurrent. Again, you need to either put those state machines into the tokio/async_std schedulers with `spawn`, or combine the state machines with `FuturesUnordered`.

Conclusion by author: > Now Go loses by over 13 times to the winner. It also loses by over 2 times to Java, which contradicts the general perception of the JVM being a memory hog and Go being lightweight.

Note that Go and Java code are not doing the same! See xargon7 comment.

Seriously impressive results from C#. I'm a JVM guy by day, and long-time admirer of C# as a language, but always assumed the two were broadly comparable performance-wise.

This is a sample of 1 usecase, (so questionable real-worldness) but the difference is really eye-opening. Congrats to the C# team!

Can we do something more real world at least? what's the cost (hetzner monthly) of maintaining 1M concurrent websocket connection where each make a query to a postgres db randomly every 1-4 seconds.

The cost wouldn't be just Memory because the network card and CPU also enter the game.

No Erlang, though. That ought to be amazingly small for that kind of synthetic benchmark.

The JS version can quickly be improved to use less memory (~10%).

    async function main() {
        const numTasks = parseInt(process.argv[2], 10);
        const taskDuration = 10000; // 10 seconds

        const tasks = Array.from({ length: numTasks }, () =>
            new Promise(resolve => setTimeout(resolve, taskDuration))
        );

        await Promise.all(tasks); // Wait for all tasks to resolve
        console.log("All tasks completed.");
    }

    main().catch(err => {
        console.error("Error occurred:", err);
    });

Reminds me of the famous

  SCO: "thread creation is about a thousand times faster than on native Linux"

  Talk is cheap. Show me the code.

https://lkml.org/lkml/2000/8/26/52

.Net AoT looks good but when you look at the compatibility matrix Android and iOS are "experimental"..

https://learn.microsoft.com/en-us/dotnet/core/deploying/nati...

This benchmark is nonsense. Apart from the fact that Go has an average Goroutine overhead of a 4kB stack (meaning an average usage of 3.9GB for 1M tasks), the code written is also in a closure, and scheduling a 2nd Goroutine in the wg.Done(), so unlike some of the others it had at least 2M function calls on the event loop stack in addition to at least 1M closure references. So yeah, it’s a great example of bad code in any language.

For those interested in Java Native Image, I previously wrote about its advantages beyond just memory footprint reduction, such as the dramatic improvement in startup time -> https://medium.com/google-cloud/cut-container-startup-time-f...

It seems there are just two clubs: you go with bare metal (Rust, C# native AOT) or you use some higher level abstraction (virtual machine, garbage collector) and then there is no significant difference between Java, Node, Go or Python.

For me Python worked surprisingly well, while Go was surprisingly high on memory consumption.

The C# version will copy the list into an array during Task.WhenAll, it may save some memory to use an array directly.

Souce: https://github.com/microsoft/referencesource/blob/master/msc...

https://eli.thegreenplace.net/2018/measuring-context-switchi... it's not a good enough example of a "task" to reflect real world usage.

NodeJS does what it was designed to do well.

Would be interesting to see the numbers for elixir/Erlang.

No Erlang/Elixir?? The author would be surprised.

It is odd that C# version doesn't increase list capacity:

List<Task> tasks = new List<Task>(numTasks);

While the idea in general is interesting, the whole site is unreadable in Firefox, text gets all over the place the graphics.

fta, please consider reading this: https://hauleth.dev/post/beam-process-memory-usage/ as well to see how erlang (elixir) fares as well.

I came here to rage (im just honest) because the go code example is bad and absolute not representative. Im coding go code for multiple years, especially alot of multithreading, and what is presented there as result is just wrong. Apart from no necessaty to use a waitgroup for threading, as many others here already have stated even with waitgroup you cacn reduce the memory significantly down to like 130mb for 1mio threads.

Also some other languages seem to be missrepresented.

Seems like someone had good intentions but no idea about the languages he tried to compare and the result is this article.

This test is no real. These languages works differently. Garbage collected and manually allocating and deallocating. If you do not configure a garbage collected language correctly it will spin out of control in memory consumption because it will just not garbage collect. If you would have configured the garbage collection to be low for java and go then go would look like rust.

Remember, folks, none of it matters if nobody uses your software.

He should have looked at Erlang.

123 MB of ram to run 1 million tasks in rust. That is mind blowing.

How would Erlang/Elixir do in this?

How do they measure the memory usage?

Do we know how memory consumption?

No baseline against UNIX processes?

NodeJS is better at memory than go?

Yet again nodejs surpasses my pre-read expectations 3rd best (generalized) for a million? Wow.

I must be missing something - isn’t Go supposed to be memory efficient? Perhaps promises and goroutines aren’t comparable?

Be sure to read the Appendix, Rust's state machine async implementation is indeed very efficient.

depends on the tasks.

I'd be ashamed to publish such shoddy "work" using any real account or name

good thing you didn't benchmark ruby because we probably couldn't see the other chart bars.

Go's performance has been neglected in past 10y. I even posted about it

https://news.ycombinator.com/item?id=41827288

To add a data point for Elixir: https://gist.github.com/neon-sunset/8fcc31d6853ebcde3b45dc7a...

Note 1: The gist is in Ukrainian, and the blog post by Steve does a much better job, but hopefully you will find this useful. Feel free to replicate the results and post them.

Note 2: The absolute numbers do not necessarily imply good/bad. Both Go and BEAM focus on userspace scheduling and its fairness. Stackful coroutines have their own advantages. I think where the blog post's data is most relevant is understanding the advantages of stackless coroutines when it comes to "highly granular" concurrency - dispatching concurrent requests, fanning out to process many small items at once, etc. In any case, I did not expect sibling comments to go onto praising Node.js, is it really that surprising for event loop based concurrency? :)

Also, if you are an Elixir aficionado and were impressed by C#'s numbers - know that they translate ~1:1 to F# now that it has task CE, just sayin'.

Here's how the program looks in F#:

    open System
    open System.Threading.Tasks

    let argv = Environment.GetCommandLineArgs()

    [1..int argv[1]]
    |> Seq.map (fun _ -> Task.Delay(TimeSpan.FromSeconds 10.0))
    |> Task.WaitAll