Closing Channels in Standard ML

(* In this example we'll use a 'jobs' channel to communicate work to be done from 
   the main function to a worker function. When we have no more jobs for the worker 
   we'll close the 'jobs' channel. *)

fun main () =
    let
        val jobs = ref []
        val done = ref false

        (* Here's the worker function. It repeatedly receives from 'jobs'.
           We use a reference to simulate a channel-like behavior. *)
        fun worker () =
            case !jobs of
                [] => if !done
                      then print "received all jobs\n"
                      else worker ()
              | j::js => (
                    jobs := js;
                    print ("received job " ^ Int.toString j ^ "\n");
                    worker ()
                )

        (* This sends 3 jobs to the worker over the 'jobs' list, then marks it as done. *)
        fun sendJobs i =
            if i > 3
            then (
                done := true;
                print "sent all jobs\n"
            )
            else (
                jobs := !jobs @ [i];
                print ("sent job " ^ Int.toString i ^ "\n");
                sendJobs (i+1)
            )
    in
        (* Start the worker *)
        Thread.spawn worker;
        
        (* Send jobs *)
        sendJobs 1;
        
        (* Wait for the worker to finish *)
        Thread.delay (Time.fromSeconds 1);
        
        (* Check if we can receive more jobs *)
        case !jobs of
            [] => print "received more jobs: false\n"
          | _  => print "received more jobs: true\n"
    end

val _ = main ()

In this Standard ML example, we’ve adapted the concept of channels to use lists and references, as Standard ML doesn’t have built-in channel support like Go. We’ve also used threads to simulate concurrent behavior.

Here’s a breakdown of the changes:

We use a reference to a list (jobs) to simulate a channel.
The worker function recursively processes jobs from the jobs list.
Instead of closing a channel, we use a done reference to indicate when all jobs have been sent.
We use Thread.spawn to start the worker function concurrently.
The sendJobs function adds jobs to the jobs list and prints messages.
We use Thread.delay to wait for the worker to finish, simulating the channel synchronization in the original example.
At the end, we check if there are any jobs left in the list to simulate checking a closed channel.

To run this program, you would need to save it in a file (e.g., closing_channels.sml) and use an Standard ML compiler or interpreter that supports threads. The exact command may vary depending on your Standard ML implementation.

This example demonstrates how to simulate channel-like behavior and concurrency in Standard ML, even though the language doesn’t have these features built-in like Go does.

Non Blocking Channel Operations in Standard ML

Range Over Channels in Standard ML

Learn X By Example

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Closing Channels in Standard ML