Horizontal pod Autoscaler scales custom metric too aggressively on GKE

According to https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/

From the most basic perspective, the Horizontal Pod Autoscaler controller operates on the ratio between desired metric value and current metric value:

desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]

From the above I understand that as long as the queue has messages the k8 HPA will continue to scale up since currentReplicas is part of the desiredReplicas calculation.

For example if:

currentReplicas = 1

currentMetricValue / desiredMetricValue = 2/1

then:

desiredReplicas = 2

If the metric stay the same in the next hpa cycle currentReplicas will become 2 and desiredReplicas will be raised to 4

I think you did a great job explaining how targetValue works with HorizontalPodAutoscalers. However, based on your question, I think you're looking for targetAverageValue instead of targetValue.

In the Kubernetes docs on HPAs, it mentions that using targetAverageValue instructs Kubernetes to scale pods based on the average metric exposed by all Pods under the autoscaler. While the docs aren't explicit about it, an external metric (like the number of jobs waiting in a message queue) counts as a single data point. By scaling on an external metric with targetAverageValue, you can create an autoscaler that scales the number of Pods to match a ratio of Pods to jobs.

Back to your example:

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: foo-hpa
  namespace: development
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: foo
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metricName: "custom.googleapis.com|rabbitmq_queue_messages_ready"
      metricSelector:
        matchLabels:
          metric.labels.queue: foo-queue
      # Aim for one Pod per message in the queue
      targetAverageValue: 1

will cause the HPA to try keeping one Pod around for every message in your queue (with a max of 10 pods).

As an aside, targeting one Pod per message is probably going to cause you to start and stop Pods constantly. If you end up starting a ton of Pods and process all of the messages in the queue, Kubernetes will scale your Pods down to 1. Depending on how long it takes to start your Pods and how long it takes to process your messages, you may have lower average message latency by specifying a higher targetAverageValue. Ideally, given a constant amount of traffic, you should aim to have a constant number of Pods processing messages (which requires you to process messages at about the same rate that they are enqueued).