DigitalOcean Kubernetes (DOKS) is a managed Kubernetes service that lets you deploy Kubernetes clusters without the complexities of handling the control plane and containerized infrastructure. Clusters are compatible with standard Kubernetes toolchains, integrate natively with DigitalOcean Load Balancers and volumes, and can be managed programmatically using the API and command line. For critical workloads, add the high-availability control plane to increase uptime with 99.95% SLA.
Cluster Autoscaling (CA) manages the number of nodes in a cluster. It monitors the number of idle pods, or unscheduled pods sitting in the
pending state, and uses that information to determine the appropriate cluster size.
Horizontal Pod Autoscaling (HPA) adds more pods and replicas based on events like sustained CPU spikes. HPA uses the spare capacity of the existing nodes and does not change the cluster’s size.
CA and HPA can work in conjunction: if the HPA attempts to schedule more pods than the current cluster size can support, then the CA responds by increasing the cluster size to add capacity. These tools can take the guesswork out of estimating the needed capacity for workloads while controlling costs and managing cluster performance.
In this tutorial, you deploy an example application that simulates workloads so you can see how the interaction between the CA and the HPA works, both when scaling up in response to demand and scaling down as load decreases.
To run the example application, you need to set up two tools:
doctl, the DigitalOcean command-line tool, v1.32.2 or higher.
kubectl, the Kubernetes command-line tool.
You can enable autoscaling on an existing cluster for this tutorial.
Alternatively, once you have
kubectl, create a new DigitalOcean Kubernetes cluster with autoscaling enabled:
doctl k8s cluster create mycluster \ --node-pool "name=mypool;auto-scale=true;min-nodes=1;max-nodes=10"
Install the DigitalOcean Kubernetes metrics server tool from the DigitalOcean Marketplace so the HPA can monitor the cluster’s resource usage. Confirm that the metrics server is installed using the following command:
kubectl top nodes
It takes a few minutes for the metrics server to start reporting the metrics. If your installation is successful, the command returns your pods’ CPU and memory statistics:
NAME CPU(cores) CPU% MEMORY(bytes) MEMORY% mypool-3z4hs 369m 36% 783Mi 49% mypool-3z4tz 84m 8% 791Mi 50% mypool-3z520 425m 42% 917Mi 58% mypool-3z52d 341m 34% 937Mi 59% mypool-3zhiq 324m 32% 856Mi 54%
Deploy the CPU-spiking service and the HPA itself by using
hpa.yaml, which defines a custom resource definition (CRD) for an HPA configured to scale to up to 20 replicas of any service on the cluster that experiences a CPU spike at or above 80%:
kubectl apply -f <path-to-hpa-yaml-file>
Test the autoscaling behavior by scheduling the load generator using
load-generator.yaml, which repeatedly sends requests to the CPU-spiking service:
kubectl apply -f <path-to-load-generator-yaml-file>
As the load generator runs, you can check the status of the HPA and CA:
kubectl describe hpa hello # Check HPA status kubectl get configmap cluster-autoscaler-status -n kube-system -oyaml # Check CA status
Continue checking the status of the HPA and CA. You can apply pressure to the cluster capacity by scaling up the load generator:
kubectl scale deployment/load-generator --replicas 2
After 5 minutes of sustained CPU spiking, the HPA starts scheduling more and more pods. Another 5 minutes after that, when the cluster runs out of capacity and the unscheduled pods start piling up, the CA kicks in to add more nodes.
Conversely, you can scale down the load generator and watch the number of pods decrease in your workload:
kubectl scale deployment/load-generator --replicas 1
After 5 minutes of lowered CPU use, the HPA starts to delete unutilized pods. Another 5 minutes after that, the CA notices the excess capacity and begins scaling down the number of nodes in the cluster as well.
In this tutorial, you repeatedly sent CPU spikes to a DigitalOcean Kubernetes cluster and tested autoscaling both when scaling up in response to demand and scaling down as load decreased.
You can customize many parts of this example’s configuration, including the kinds of events that trigger an action from the HPA and how long they need to last to trigger a response. In general, you need to configure the HPA to balance responsiveness (being sensitive enough for timely responses to load changes) against thrashing (being too sensitive and causing wild fluctuations). For more details on configuring HPAs, see Horizontal Pod Autoscaler in the Kubernetes documentation.