After that, we create an orders queue in our namespace:

We need to be able to connect to our queue, so we create a new authorization rule. In this case, we will assign Management permissions given this is a requirement for KEDA.

Once the authorization rule is created, we can list the connection string as following:

Create a base64 representation of primaryConnectionString :

Create a secret to deploy in Kubernetes that contains our connection string:

This secret will be used by our order processor and KEDA to connect to the queue.

Save the secret declaration in deploy/deploy-secret.yaml.

Deploying our Service Bus secret in Kubernetes

We will start by creating a new Kubernetes namespace to run our order processor in:

Before we can connect to our queue, we need to deploy the secret which contains the Service Bus connection string to the queue.

Once created, you should be able to retrieve the secret:

Deploying our order processor pod

We are ready to go! We will start by creating a Kubernetes deployment.

The deployment will schedule a pod running our order processor based on tomkerkhove/keda-sample-dotnet-worker-servicebus-queue Docker image.

As you can see it passes a KEDA_SERVICEBUS_QUEUE_CONNECTIONSTRING environment variable which contains the value of the secret we’ve just deployed.

Kubernetes will automatically decode it and pass the raw connection string.

Save the deployment declaration and deploy it:

Once created, you will see that our deployment shows up with one pod:

Defining how we want to autoscale with a ScaledObject

Now that our app is running we can start automatically scaling it!

By deploying a ScaledObject you tell KEDA what deployment you want to scale and how:

In our case we define that we want to use the azure-servicebus scale trigger and what our criteria is. For our scenario we’d like to scale out if there are 5 or more messages in the orders queue with a maximum of 10 concurrent replicas which is defined via maxReplicaCount.

KEDA will use the KEDA_SERVICEBUS_QUEUE_CONNECTIONSTRING environment variable on our order-processor Kubernetes Deployment to connect to Azure Service Bus. This allows us to avoid duplication of configuration.

Note – If we were to use a sidecar, we would need to define containerName which contains this environment variable.

Save the ScaledObject declaration and deploy it:

Once the ScaledObject is deployed you’ll notice that we don’t have any pods running anymore:

This is because our queue is empty and KEDA scaled it down until there is work to do.

In that case, let’s generate some!

Publishing messages to the queue

The following job will send messages to the “orders” queue on which the order processor is listening to. As the queue builds up, KEDA will help the horizontal pod autoscaler add more and more pods until the queue is drained. The order generator will allow you to specify how many messages you want to queue.

First, you should clone the project:

Configure the connection string in the tool via your favorite text editor, in this case via Visual Studio Code:

Next, you can run the order generator via the CLI:

Now that the messages are generated, you’ll see that KEDA starts automatically scaling out your deployment:

Eventually, we will have 10 pods running processing messages in parallel:

You can look at the logs for a given processor as following:

Once all the messages have been processed KEDA will scale the deployment back to 0 pod instances.

Time to clean up

Don’t forget to clean up your resources!

Conclusion

We have easily deployed a .NET Core 3.0 Worker on Kubernetes which was processing messages from Service Bus.

Once we’ve deployed a ScaledObject for our Kubernetes deployment it started scaling the pods out and in according to the queue depth.

We could very easily plug in autoscaling for our existing application without making any changes!

Thanks for reading,

Tom.