Channels in Standard ML

In Standard ML, we don’t have built-in channels or goroutines like Go. However, we can simulate similar behavior using threads and mutable references. Here’s an example that demonstrates a similar concept:

structure Channel = struct
  type 'a channel = 'a option ref * unit -> unit

  fun make () = (ref NONE, fn () => ())

  fun send (ch, v) =
    let val (r, signal) = ch
    in r := SOME v; signal ()
    end

  fun receive (ch) =
    let val (r, _) = ch
    in case !r of
         SOME v => (r := NONE; v)
       | NONE => raise Fail "Empty channel"
    end
end

fun main () =
  let
    val messages = Channel.make ()

    val _ = Thread.spawn (fn () => Channel.send (messages, "ping"))

    val msg = Channel.receive messages
    val _ = print (msg ^ "\n")
  in
    ()
  end

val _ = main ()

In this Standard ML code:

1. We define a Channel structure that simulates channels. It uses a mutable reference (ref) to store the value and a unit function for signaling.

2. The make function creates a new channel, similar to make(chan string) in Go.

3. The send function simulates sending a value into a channel.

4. The receive function simulates receiving a value from a channel.

5. In the main function, we create a channel called messages.

6. We use Thread.spawn to start a new thread, which sends the “ping” message to the channel. This is similar to using a goroutine in Go.

7. We then receive the message from the channel and print it.

When we run this program, the “ping” message is successfully passed from one thread to another via our simulated channel.

$ sml channels.sml
ping

Note that this implementation is a simplified simulation and doesn’t have all the properties of Go channels. In particular, it doesn’t implement blocking behavior. In a real-world scenario, you might want to use more sophisticated concurrency primitives available in Standard ML or its extensions.

Also, Standard ML doesn’t have a built-in concurrency model like Go’s goroutines. The Thread structure used here is typically provided by the Standard ML of New Jersey (SML/NJ) implementation. Other Standard ML implementations might have different concurrency primitives or require additional libraries.