I am trying to horizontally autoscale a workload not only by custom metrics but also by algorithm that differs from algorithm described here

1/ is that possible?

2/ if it is not, and assuming i don't mind creating a container that does the autoscaling for me instead of HPA, what API should i call to do the equivalent of kubectl scale deployments/<name> --replicas=<newDesired> ?

here's the use-case:

1/ the workload consumes a single request from a queue, handles them, when done removes the item it handled, and consumes the next message.

2/ when there's more than 0 messages ready - I'd like to scale up to the number of messages ready (or max scale if it is larger). when there's 0 messages being processed - i'd like to scale down to 0.

getting the messages ready/ messages being processed to metrics server is not an issue.

getting HPA to scale by "messages ready" is not an issue either.

but...

HPA algorithm scales gradually... when i place 10 items in queue - it first to 4, then to 8 then to 10.

it also scales down gradually, and when it scales down it can terminate a pod that was processing - thus increasing the "ready" and causing a scale-up.

a node.js code that i would have run had i known the api to call (intead of HPA):

    let desiredToSet = 0;
    if (!readyMessages && !processingMessages) {
      //if we have nothing in queue and all workers completed their work - we can scale down to minimum
      //we like it better than reducing slowly as this way we are not risking killing a worker that's working
      desiredToSet = config.minDesired;
    }
    else {
      //messages ready in the queue, increase number of workers up to max allowed
      desiredToSet = Math.max(Math.min(readyMessages + processingMessages, config.maxDesired), currentDeploymentReplicas);
    }
    //no point in sending a request to change, if nothing changed
    if (desiredToSet !== currentDeploymentReplicas) {
      <api to set desiredToSet of deployment to come here>;
    }