Autoscaling worked fine (kubernetes added new 1cpu/2gbRAM droplet/node to cluster automatically) when i started a kubernetes Job with
memory: "800Mi", parallelism: 2 but when i tried to start a job with requested
memmory: "2000Mi, parallelism: 2 it showing error in job’s event logs
pod didn't trigger scale-up (it wouldn't fit if a new node is added): 1 Insufficient memory
kubectl apply -f job.yaml
apiVersion: batch/v1 kind: Job metadata: name: scaling-test spec: parallelism: 2 template: metadata: name: scaling-test spec: containers: - name: debian image: debian command: ["/bin/sh","-c"] args: ["sleep 300"] resources: requests: cpu: "100m" memory: "1900Mi" restartPolicy: Never
My assumption was, kubernetes while autoscaling will add new node with respect to job’s requested resource requirements.(add 1cpu/3gbRAM node) any idea how to make kubernetes autoscale and provision a node based on job’s requested resources?
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The autoscaling task adds nodes to the pool that requires additional compute/memory resources. The node type is determined by the pool the settings and not by the autoscaling rules.
From this, you can see that you need to ensure that your configured node is large enough to handle your largest pod.
As the two answers above mention. There is overhead from both the containerized infrastructure and the system that limit the amount of RAM left for your autoscaled workload. Can you verify that this error is resolved when using a larger node size?
You can find an estimate of the allocateable memory for node sizes in our documentation here: https://www.digitalocean.com/docs/kubernetes/#allocatable-memory
Let me know if you have any further questions.
John Kwiatkoski Senior Developer Support Engineer - Kubernetes