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One of the primary reasons organizations adopt Kubernetes is its ability to scale applications dynamically based on traffic. Kubernetes operates on three different dimensions of scaling.
The HPA automatically scales the number of Pods in a Deployment based on observed CPU utilization (or, with custom metrics, things like queue length or HTTP request rate).
# Autoscale a deployment to maintain 50% CPU usage, between 1 and 10 pods
kubectl autoscale deployment my-app --cpu-percent=50 --min=1 --max=10
If traffic spikes, HPA will create more Pods. When traffic drops, it will terminate them to save resources.
Sometimes, adding more Pods (Horizontal scaling) doesn't solve the problem, especially for stateful applications like legacy databases. The VPA automatically adjusts the CPU and Memory requests and limits for your Pods.
Instead of adding a new Pod, the VPA restarts the existing Pod but assigns it more RAM or CPU from the host node.
What happens if the HPA requests 10 new Pods, but your physical nodes don't have enough CPU/RAM left to run them? The Pods will be stuck in a Pending state.
The Cluster Autoscaler watches for Pods that cannot be scheduled due to resource constraints. When it sees them, it automatically talks to your cloud provider (AWS, GCP, Azure) and spins up brand new Virtual Machines to join the cluster as worker nodes. Once the traffic dies down, it terminates the idle virtual machines to save you money.
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