Worker Pools in Elm

Our example demonstrates how to implement a worker pool using Elm’s concurrency model. In Elm, we use the Task type to represent asynchronous operations and the Process module to manage concurrent tasks.

import Task exposing (Task)
import Process
import Time

-- Simulates an expensive task
worker : Int -> Int -> Task Never Int
worker id job =
    Task.andThen (\_ ->
        Process.sleep 1000
            |> Task.andThen (\_ ->
                Task.succeed (job * 2)
            )
    ) (Task.succeed (Debug.log ("Worker " ++ String.fromInt id ++ " started job") job))

-- Creates a pool of workers
workerPool : Int -> List Int -> Task Never (List Int)
workerPool numWorkers jobs =
    let
        distributeJobs : List Int -> List (List Int)
        distributeJobs remainingJobs =
            case remainingJobs of
                [] ->
                    []
                _ ->
                    List.take numWorkers remainingJobs :: distributeJobs (List.drop numWorkers remainingJobs)

        processJobBatch : Int -> List Int -> Task Never (List Int)
        processJobBatch workerId batch =
            Task.sequence (List.indexedMap (\index job -> worker (workerId * 1000 + index) job) batch)
    in
    distributeJobs jobs
        |> List.indexedMap processJobBatch
        |> Task.sequence
        |> Task.map List.concat

main : Program () () msg
main =
    Platform.worker
        { init = \_ -> ( (), Cmd.none )
        , update = \_ model -> ( model, Cmd.none )
        , subscriptions = \_ -> Sub.none
        }

-- Example usage
init : Task Never ()
init =
    let
        numJobs = 5
        jobs = List.range 1 numJobs
    in
    workerPool 3 jobs
        |> Task.andThen (\results ->
            Task.succeed (Debug.log "Results" results)
        )
        |> Task.perform (\_ -> ())

In this Elm implementation:

We define a worker function that simulates an expensive task. It takes an ID and a job number, logs when it starts, sleeps for a second, and then returns the job number multiplied by 2.
The workerPool function creates a pool of workers. It distributes jobs among the workers and processes them concurrently.
We use Task.sequence to run multiple tasks concurrently and collect their results.
The distributeJobs helper function splits the jobs into batches for each worker.
The processJobBatch function processes a batch of jobs for a specific worker.
In the init function, we create 5 jobs and process them using a pool of 3 workers.

To run this program, you would typically set up an Elm application and call the init function. The results will be logged to the console.

Note that Elm’s concurrency model is different from Go’s. Elm uses a more functional approach with Tasks and doesn’t have explicit concepts like goroutines or channels. However, this implementation achieves a similar result of processing jobs concurrently using a worker pool.

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Worker Pools in Elm