Part of the "Why use F#?" series (link)

Asynchronous programming

Encapsulating a background task with the Async class

In this post we’ll have a look at a few ways to write asynchronous code in F#, and a very brief example of parallelism as well.

Traditional asynchronous programming

As noted in the previous post, F# can directly use all the usual .NET suspects, such as Thread AutoResetEvent, BackgroundWorker and IAsyncResult.

Let’s see a simple example where we wait for a timer event to go off:

open System

let userTimerWithCallback =
    // create an event to wait on
    let event = new System.Threading.AutoResetEvent(false)

    // create a timer and add an event handler that will signal the event
    let timer = new System.Timers.Timer(2000.0)
    timer.Elapsed.Add (fun _ -> event.Set() |> ignore )

    printfn "Waiting for timer at %O" DateTime.Now.TimeOfDay

    // keep working
    printfn "Doing something useful while waiting for event"

    // block on the timer via the AutoResetEvent
    event.WaitOne() |> ignore

    printfn "Timer ticked at %O" DateTime.Now.TimeOfDay

This shows the use of AutoResetEvent as a synchronization mechanism.

  • A lambda is registered with the Timer.Elapsed event, and when the event is triggered, the AutoResetEvent is signalled.
  • The main thread starts the timer, does something else while waiting, and then blocks until the event is triggered.
  • Finally, the main thread continues, about 2 seconds later.

The code above is reasonably straightforward, but does require you to instantiate an AutoResetEvent, and could be buggy if the lambda is defined incorrectly.

Introducing asynchronous workflows

F# has a built-in construct called “asynchronous workflows” which makes async code much easier to write. These workflows are objects that encapsulate a background task, and provide a number of useful operations to manage them.

Here’s the previous example rewritten to use one:

open System
//open Microsoft.FSharp.Control  // Async.* is in this module.

let userTimerWithAsync =

    // create a timer and associated async event
    let timer = new System.Timers.Timer(2000.0)
    let timerEvent = Async.AwaitEvent (timer.Elapsed) |> Async.Ignore

    // start
    printfn "Waiting for timer at %O" DateTime.Now.TimeOfDay

    // keep working
    printfn "Doing something useful while waiting for event"

    // block on the timer event now by waiting for the async to complete
    Async.RunSynchronously timerEvent

    // done
    printfn "Timer ticked at %O" DateTime.Now.TimeOfDay

Here are the changes:

  • the AutoResetEvent and lambda have disappeared, and are replaced by let timerEvent = Control.Async.AwaitEvent (timer.Elapsed), which creates an async object directly from the event, without needing a lambda. The ignore is added to ignore the result.
  • the event.WaitOne() has been replaced by Async.RunSynchronously timerEvent which blocks on the async object until it has completed.

That’s it. Both simpler and easier to understand.

The async workflows can also be used with IAsyncResult, begin/end pairs, and other standard .NET methods.

For example, here’s how you might do an async file write by wrapping the IAsyncResult generated from BeginWrite.

let fileWriteWithAsync =

    // create a stream to write to
    use stream = new System.IO.FileStream("test.txt",System.IO.FileMode.Create)

    // start
    printfn "Starting async write"
    let asyncResult = stream.BeginWrite(Array.empty,0,0,null,null)

	// create an async wrapper around an IAsyncResult
    let async = Async.AwaitIAsyncResult(asyncResult) |> Async.Ignore

    // keep working
    printfn "Doing something useful while waiting for write to complete"

    // block on the timer now by waiting for the async to complete
    Async.RunSynchronously async

    // done
    printfn "Async write completed"

Creating and nesting asynchronous workflows

Asynchronous workflows can also be created manually. A new workflow is created using the async keyword and curly braces. The braces contain a set of expressions to be executed in the background.

This simple workflow just sleeps for 2 seconds.

let sleepWorkflow  = async{
    printfn "Starting sleep workflow at %O" DateTime.Now.TimeOfDay
    do! Async.Sleep 2000
    printfn "Finished sleep workflow at %O" DateTime.Now.TimeOfDay

Async.RunSynchronously sleepWorkflow

Note: the code do! Async.Sleep 2000 is similar to Thread.Sleep but designed to work with asynchronous workflows.

Workflows can contain other async workflows nested inside them. Within the braces, the nested workflows can be blocked on by using the let! syntax.

let nestedWorkflow  = async{

    printfn "Starting parent"
    let! childWorkflow = Async.StartChild sleepWorkflow

    // give the child a chance and then keep working
    do! Async.Sleep 100
    printfn "Doing something useful while waiting "

    // block on the child
    let! result = childWorkflow

    // done
    printfn "Finished parent"

// run the whole workflow
Async.RunSynchronously nestedWorkflow

Cancelling workflows

One very convenient thing about async workflows is that they support a built-in cancellation mechanism. No special code is needed.

Consider a simple task that prints numbers from 1 to 100:

let testLoop = async {
    for i in [1..100] do
        // do something
        printf "%i before.." i

        // sleep a bit
        do! Async.Sleep 10
        printfn "..after"

We can test it in the usual way:

Async.RunSynchronously testLoop

Now let’s say we want to cancel this task half way through. What would be the best way of doing it?

In C#, we would have to create flags to pass in and then check them frequently, but in F# this technique is built in, using the CancellationToken class.

Here an example of how we might cancel the task:

open System
open System.Threading

// create a cancellation source
use cancellationSource = new CancellationTokenSource()

// start the task, but this time pass in a cancellation token
Async.Start (testLoop,cancellationSource.Token)

// wait a bit

// cancel after 200ms

In F#, any nested async call will check the cancellation token automatically!

In this case it was the line:

do! Async.Sleep(10)

As you can see from the output, this line is where the cancellation happened.

Composing workflows in series and parallel

Another useful thing about async workflows is that they can be easily combined in various ways: both in series and in parallel.

Let’s again create a simple workflow that just sleeps for a given time:

// create a workflow to sleep for a time
let sleepWorkflowMs ms = async {
    printfn "%i ms workflow started" ms
    do! Async.Sleep ms
    printfn "%i ms workflow finished" ms

Here’s a version that combines two of these in series:

let workflowInSeries = async {
    let! sleep1 = sleepWorkflowMs 1000
    printfn "Finished one"
    let! sleep2 = sleepWorkflowMs 2000
    printfn "Finished two"

Async.RunSynchronously workflowInSeries

And here’s a version that combines two of these in parallel:

// Create them
let sleep1 = sleepWorkflowMs 1000
let sleep2 = sleepWorkflowMs 2000

// run them in parallel
[sleep1; sleep2]
    |> Async.Parallel
    |> Async.RunSynchronously
Note: The #time command toggles the timer on and off. It only works in the interactive window, so this example must be sent to the interactive window in order to work corrrectly.

We’re using the #time option to show the total elapsed time, which, because they run in parallel, is 2 secs. If they ran in series instead, it would take 3 seconds.

Also you might see that the output is garbled sometimes because both tasks are writing to the console at the same time!

This last sample is a classic example of a “fork/join” approach, where a number of a child tasks are spawned and then the parent waits for them all to finish. As you can see, F# makes this very easy!

Example: an async web downloader

In this more realistic example, we’ll see how easy it is to convert some existing code from a non-asynchronous style to an asynchronous style, and the corresponding performance increase that can be achieved.

So here is a simple URL downloader, very similar to the one we saw at the start of the series:

open System.Net
open System
open System.IO

let fetchUrl url =
    let req = WebRequest.Create(Uri(url))
    use resp = req.GetResponse()
    use stream = resp.GetResponseStream()
    use reader = new IO.StreamReader(stream)
    let html = reader.ReadToEnd()
    printfn "finished downloading %s" url

And here is some code to time it:

// a list of sites to fetch
let sites = ["";

#time                     // turn interactive timer on
sites                     // start with the list of sites
|> fetchUrl      // loop through each site and download
#time                     // turn timer off

Make a note of the time taken, and let’s see if we can improve on it!

Obviously the example above is inefficient – only one web site at a time is visited. The program would be faster if we could visit them all at the same time.

So how would we convert this to a concurrent algorithm? The logic would be something like:

  • Create a task for each web page we are downloading, and then for each task, the download logic would be something like:
    • Start downloading a page from a website. While that is going on, pause and let other tasks have a turn.
    • When the download is finished, wake up and continue on with the task
  • Finally, start all the tasks up and let them go at it!

Unfortunately, this is quite hard to do in a standard C-like language. In C# for example, you have to create a callback for when an async task completes. Managing these callbacks is painful and creates a lot of extra support code that gets in the way of understanding the logic. There are some elegant solutions to this, but in general, the signal to noise ratio for concurrent programming in C# is very high*.

* As of the time of this writing. Future versions of C# will have the await keyword, which is similar to what F# has now.

But as you can guess, F# makes this easy. Here is the concurrent F# version of the downloader code:

open Microsoft.FSharp.Control.CommonExtensions
                                        // adds AsyncGetResponse

// Fetch the contents of a web page asynchronously
let fetchUrlAsync url =
    async {
        let req = WebRequest.Create(Uri(url))
        use! resp = req.AsyncGetResponse()  // new keyword "use!"
        use stream = resp.GetResponseStream()
        use reader = new IO.StreamReader(stream)
        let html = reader.ReadToEnd()
        printfn "finished downloading %s" url

Note that the new code looks almost exactly the same as the original. There are only a few minor changes.

  • The change from “use resp = ” to “use! resp =” is exactly the change that we talked about above – while the async operation is going on, let other tasks have a turn.
  • We also used the extension method AsyncGetResponse defined in the CommonExtensions namespace. This returns an async workflow that we can nest inside the main workflow.
  • In addition the whole set of steps is contained in the “async {...}” wrapper which turns it into a block that can be run asynchronously.

And here is a timed download using the async version.

// a list of sites to fetch
let sites = ["";

#time                      // turn interactive timer on
|> fetchUrlAsync  // make a list of async tasks
|> Async.Parallel          // set up the tasks to run in parallel
|> Async.RunSynchronously  // start them off
#time                      // turn timer off

The way this works is:

  • fetchUrlAsync is applied to each site. It does not immediately start the download, but returns an async workflow for running later.
  • To set up all the tasks to run at the same time we use the Async.Parallel function
  • Finally we call Async.RunSynchronously to start all the tasks, and wait for them all to stop.

If you try out this code yourself, you will see that the async version is much faster than the sync version. Not bad for a few minor code changes! Most importantly, the underlying logic is still very clear and is not cluttered up with noise.

Example: a parallel computation

To finish up, let’s have another quick look at a parallel computation again.

Before we start, I should warn you that the example code below is just to demonstrate the basic principles. Benchmarks from “toy” versions of parallelization like this are not meaningful, because any kind of real concurrent code has so many dependencies.

And also be aware that parallelization is rarely the best way to speed up your code. Your time is almost always better spent on improving your algorithms. I’ll bet my serial version of quicksort against your parallel version of bubblesort any day! (For more details on how to improve performance, see the optimization series)

Anyway, with that caveat, let’s create a little task that chews up some CPU. We’ll test this serially and in parallel.

let childTask() =
    // chew up some CPU.
    for i in [1..1000] do
        for i in [1..1000] do
            do "Hello".Contains("H") |> ignore
            // we don't care about the answer!

// Test the child task on its own.
// Adjust the upper bounds as needed
// to make this run in about 0.2 sec

Adjust the upper bounds of the loops as needed to make this run in about 0.2 seconds.

Now let’s combine a bunch of these into a single serial task (using composition), and test it with the timer:

let parentTask =
    |> List.replicate 20
    |> List.reduce (>>)


This should take about 4 seconds.

Now in order to make the childTask parallelizable, we have to wrap it inside an async:

let asyncChildTask = async { return childTask() }

And to combine a bunch of asyncs into a single parallel task, we use Async.Parallel.

Let’s test this and compare the timings:

let asyncParentTask =
    |> List.replicate 20
    |> Async.Parallel

|> Async.RunSynchronously

On a dual-core machine, the parallel version is about 50% faster. It will get faster in proportion to the number of cores or CPUs, of course, but sublinearly. Four cores will be faster than one core, but not four times faster.

On the other hand, as with the async web download example, a few minor code changes can make a big difference, while still leaving the code easy to read and understand. So in cases where parallelism will genuinely help, it is nice to know that it is easy to arrange.


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