Fire-and-forget in Service Fabric actors

At the recent Webscale Architecture meetup we discussed two implementations of the Actor model in the .NET ecosystem: Akka.NET and Azure Service Fabric Actors. One important discussion was around Ask vs Tell call model. With Tell model, the Sender just sends the message to the Recepient without waiting for a result to come back. Ask model means the Sender will at some point get a response back from the Receiver, potencially blocking its own execution.

The default model of Akka.NET is Tell:

Tell: Fire-forget

This is the preferred way of sending messages. No blocking waiting for a message. This gives the best concurrency and scalability characteristics.

On the contrary, the default model for Service Fabric Actors is RPC-like Ask model. Let’s have a close look at this model, and then see how we can implement Tell (or Fire-and-Forget) model.

Actor definition starts with an interface:

public interface IHardWorkingActor : IActor
    Task DoWork(string payload);

As you can see, the method does not return any useful data, which means the client code isn’t really interested in waiting for the operation to complete. Here’s how we implement this interface in the Actor class:

public class HardWorkingActor : Actor, IHardWorkingActor
    public async Task DoWork(string payload)
        ActorEventSource.Current.ActorMessage(this, "Doing Work");
        await Task.Delay(500);

This test implementation simulates the hard work by means of an artificial 500 ms delay.

Now, let’s look at the client code. Let’s say, the client receives the payloads from a queue or a web front-end and needs to go as fast as possible. It gets a payload, creates an actor proxy to dispatch the payload to, then it just wants to continue with the next payload. Here is the “Ask” implementation based on the Service Fabric samples:

int i = 0;
var timer = new Stopwatch();
while (true)
    var proxy = ActorProxy.Create<IHardWorkingActor>(ActorId.NewId(), "fabric:/Application1");
    await proxy.DoWork($"Work ${i++}");
    Console.WriteLine($@"Sent work to Actor {proxy.GetActorId()},
                         rate is {i / timer.Elapsed.TotalSeconds}/sec");

Note an await operator related to every call. That means that the client will block until the actor work is complete. When we run the client, no surprise that we get the rate of about 2 messages per second:

Sent work to Actor 1647857287613311317, rate is 1,98643230380293/sec

That’s not very exciting. What we want instead is to tell the actor to do the work and immediately proceed to the next one. Here’s how the client call should look like:

proxy.DoWork($"Work ${i++}").FireAndForget();

Instead of await-ing, we make a Task, pass it to some (not yet existing) extension method and proceed immediately. It appears that the implementation of such extension method is trivial:

public static class TaskHelper
    public static void FireAndForget(this Task task)
        Task.Run(async() => await task).ConfigureAwait(false);

The result looks quite different from what we had before:

Sent work to Actor -8450334792912439527, rate is 408,484162592517/sec

400 messages per second, which is some 200x difference…

The conclusions are simple:

  • Service Fabric is a powerful platform and programming paradigm which doesn’t limit your choice of communication patterns

  • Design the communication models carefully based on your use case, don’t take the defaults for granted

Cloud developer and researcher.
Software engineer at Pulumi. Microsoft Azure MVP.

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