Improper scaling of dask workers

So when using dask cluster on kubernetes with adaptive scaling there are two issues I noticed.

One is repeated scale up or scale down request happening but no scaling happens as result of it even though cluster scaling permission is given to the kubernetes cluster. This image shows the issue. Scale down request was fired every 5 seconds but no scale down happened. Same goes for scale up.

Second issue i came across was regarding kopf.

@kopf.timer("daskautoscaler.kubernetes.dask.org", interval=5.0)
async def daskautoscaler_adapt(spec, name, namespace, logger, **kwargs):
    async with kubernetes.client.api_client.ApiClient() as api_client:
        coreapi = kubernetes.client.CoreV1Api(api_client)

        pod_ready = False
        try:
            scheduler_pod = await coreapi.read_namespaced_pod(
                f"{spec['cluster']}-scheduler", namespace

This is the function that gets called for adaptive scaling which does a replica scale.

While this has happened and cluster is trying to scale to maximum possible workers another function mentioned below pitches in and gives a scale down command because of which cluster never scales the way we want.

@kopf.on.field("daskworkergroup.kubernetes.dask.org", field="spec.worker.replicas")
async def daskworkergroup_replica_update(
        name, namespace, meta, spec, new, body, logger, initial=False, **kwargs
):
    if not initial:
        return
    cluster_name = spec["cluster"]

    # Replica updates can come in quick succession and the changes must be applied atomically to ensure
    # the number of workers ends in the correct state
    async with worker_group_scale_locks[f"{namespace}/{name}"]:
        async with kubernetes.client.api_client.ApiClient() as api_client:
            customobjectsapi = kubernetes.client.CustomObjectsApi(api_client)
            corev1api = kubernetes.client.CoreV1Api(api_client)

dask / dask-kubernetes

Improper scaling of dask workers #677