Autoscaling on Kubernetes
Scale pods and nodes automatically under real load.
0 of 6 steps
What you will end up with
Horizontal pod autoscaling
Cluster node autoscaling
A validated load test
Build steps
0/6Add CPU and memory requests and limits to every container in the pod spec. This tells the scheduler exactly how much shelf space each item needs before it tries to stock the shelf.
Create an HPA targeting your Deployment with a metric like average CPU at 70%. The HPA is a thermostat: too hot, meaning too much load, and it turns on more units, meaning more pods.
Install a metrics adapter so the HPA can scale on something like requests per second instead of just CPU. This swaps a basic thermostat for one that reacts to something more specific than plain room temperature.
Deploy the cluster autoscaler add-on and tag your node group so it can add or remove whole nodes, not just pods. This calls in more delivery trucks when the warehouse itself runs out of floor space, not just more staff.
Send steadily increasing traffic with a load-testing tool and watch pod count climb in real time. Turn up the heat on purpose to see whether the thermostat actually responds.
Adjust the HPA’s target percentage or add a stabilization window based on how fast or jumpy the scaling was. Nudge the thermostat’s setpoint after watching it overshoot or undershoot the first time.
Before you start