In my previous post Custom Autoscaling of Azure App Service with a Function App I've created a Function App which watches a Service Bus Subscription backlog and adjusts the scale of App Service based on the observed load.

It works fine but there are two minor issues that I would like to address in this article:

  • Scaling Logic function from that workflow needs to preserve state between calls. I used Table Storage bindings for that, which proved to be a bit verbose and low level: I needed to manage conversion to entity and JSON serialization myself;

  • There is no feedback from Scaler function (which executes the change) back to Scaling Logic function. Thus, if scaling operation is slow or fails, there is no easy way to notify the logic about that.

Let's see how these issues can be solved with Azure Durable Functions.

Meet Durable Functions

Microsoft has recently announced the preview of Durable Functions:

Durable Functions is an Azure Functions extension for building long-running, stateful function orchestrations in code using C# in a serverless environment.

The library is built on top of Durable Task Framework and introduces several patterns for Function coordination and stateful processing. Please go read the documentation, it's great and has some very useful examples.

I decided to give Durable Functions a try for my autoscaling workflow. Feel free to refer to the first part to understand my goals and the previous implementation.

Architecture

The flow of metric collection, scaling logic and scaling action stays the same. The state and cross-function communication aspects are now delegated to Durable Functions, so the diagram becomes somewhat simpler:

Autoscaling Architecture

The blue sign on Scaling Logic function denotes its statefulness.

Let's walk through the functions implementation to see how the workflow plays out.

This time I'll start with Scaler function and then flow from right to left to make the explanation more clear.

Scaler

Scaler function applies the scaling decisions to the Azure resource, App Service Plan in my case. I've extracted App Service related code to a helper, to keep the function minimal and clean. You can see the full code in my github repo.

Scaler function is triggered by Durable Function's ActivityTrigger. That basically means that it's ready to be called from other functions. Here is the code:

[FunctionName(nameof(Scaler))]
public static int Scaler([ActivityTrigger] DurableActivityContext context)
{
    var action = context.GetInput<ScaleAction>();

    var newCapacity = ScalingHelper.ChangeAppServiceInstanceCount(
        action.ResourceName,
        action.Type == ScaleActionType.Down ? -1 : +1);

    return newCapacity;
}

In order to receive an input value, I utilize context.GetInput() method. I believe that the team is working on support of custom classes (ScaleAction in my case) directly as function parameters.

The function then executes the scale change and returns back the new capacity of App Service Plan. Note that this is new: we were not able to return values in the previous implementation.

Scaling Logic

Scaling Logic is using Stateful Actor pattern. One instance of such actor is created for each scalable resource (I only use 1 now). Here is the implementation (again, simplified for readability):

[FunctionName(nameof(ScalingLogic))]
public static async Task<ScalingState> ScalingLogic(
    [OrchestrationTrigger] DurableOrchestrationContext context, 
    TraceWriter log)
{
    var state = context.GetInput<ScalingState>();

    var metric = await context.WaitForExternalEvent<Metric>(nameof(Metric));

    UpdateHistory(state.History, metric.Value);
    ScaleAction action = CalculateScalingAction(state);

    if (action != null)
    {
        var result = await context.CallFunctionAsync<int>(nameof(Scaler), action);
        log.Info($"Scaling logic: Scaled to {result} instances.");
        state.LastScalingActionTime = context.CurrentUtcDateTime;
    }

    context.ContinueAsNew(state);
    return state;
}

Here is how it works:

  • Function is bound to OrchestrationTrigger, yet another trigger type from Durable Functions;

  • It loads durable state from the received context;

  • It then waits for an external event called Metric (to be sent by Collector function, see the next section);

  • When an event is received, the function updates its state and calculates if a scaling action is warranted;

  • If yes, it calls Scaler function and sends the scale action. It expects an integer result, denoting the new amount of instances;

  • It then calls ContinueAsNew method to start a new iteration of the actor loop, providing the updated state.

One important note: the orchestrated function has to be deterministic. That means, for example, that DateTime.Now is not allowed to be used. I use context.CurrentUtcDateTime instead for time-related calculations.

The implementation of this function solves both problems that I mentioned in the introduction. We do not manage state storage and serialization manually, and we now have the ability to get feedback from Scaler function.

Metrics Collector

I've extracted Service Bus related code to a helper to keep the code sample minimal and clean. You can see the full code in my github repo.

Here is the remaining implementation of Metric Collector:

[FunctionName(nameof(MetricCollector))]
public static async Task MetricCollector(
    [TimerTrigger("0 */1 * * * *")] TimerInfo myTimer,
    [OrchestrationClient] DurableOrchestrationClient client,
    TraceWriter log)
{
    var resource = Environment.GetEnvironmentVariable("ResourceToScale");

    var status = await client.GetStatusAsync(resource);
    if (status == null)
    {
        await client.StartNewAsync(nameof(ScalingLogic), resource, new ScalingState());
    }
    else
    {
        var metric = ServiceBusHelper.GetSubscriptionMetric(resource);
        log.Info($"Collector: Current metric value is {metric.Value.Value} at {DateTime.Now}");
        await client.RaiseEventAsync(resource, nameof(Metric), metric);
    }
}

It's still a timer-triggered "normal" (non-durable) function, but now it also has an additional binding to OrchestrationClient. This client is used to communicate metric data to the Scaling Logic.

With the current implementation, Metric Collector also has a second responsibility: actor instance management. At every iteration, it queries for the current status of corresponding actor. If that is null, Collector creates a new instance with initial empty state.

To my liking, this aspect is a bit unfortunate, but it seems to be required with the current implementation of Durable Functions framework. See my related question on github.

Conclusions

I adjusted the initial flow of autoscaling functions to use Durable Functions library. It made the state management look more straightforward, and also allowed direct communication between two functions in strongly-typed request-response manner.

The resulting code is relatively clear and resembles the typical structure of async-await code that C# developers are used to.

There are some downsides that I found about Durable Functions too:

  • This is a very early preview, so there are some implementation issues. A couple times I managed to put my functions into a state where they were stuck and no calls could be made anymore. The only way I could get out of there is by clearing some blobs in the Storage Account;

  • The actor instance management story feels raw. The function, which needs to send events to actors, has to manage their lifecycle and instance IDs. I would need to add some more checks to make the code production ready, e.g. to restart actors if they end up in faulty state;

  • There are some concurrency issues in function-to-function communication to be resolved;

  • Some discipline is required to keep Durable functions side-effect free and deterministic. The multiple executions caused by awaits and replays are counter-intuitive (at least for novice devs), and thus error-prone.

Having said that, I believe Durable Functions can be a very useful abstraction to simplify some of the more advanced scenarios and workflows. I look forward to further iterations of the library, and I will keep trying it out for more scenarios.