When you have a web application running, the major concern that most people underestimate is that if the resources are enough to serve the expected load. Recently a government website got launched in my country and it was unresponsive due to the traffic spike it got. The performance aspect of a web application must be considered very well.
\nWe have created a Rasberry Pi cluster in the previous blog posts. It is expected to you have that set up or a Kubernetes cluster configured from a cloud provider. I will explain the content here depending on the Raspberry Pi cluster setup.
\nIn Kubernetes when you do a deployment it can be configured for autoscaling using HorizontalPodAutoscaler (HPA). It can take control of the number of replicas running given the resource constraints for the deployment.
The first thing you need is to set up the metrics server which gives container resource metrics for Kubernetes built-in autoscaling pipelines. Go to kubernetes-sigs/metrics-server releases page and download the component.yaml under the latest release.
You need to add the following two lines to the spec section, which will allow the metrics server to work with self-signed certificates:
\nspec:\n hostNetwork: true\n containers:\n - args:\n ...\n ...\n - --kubelet-insecure-tls=trueNow run the following command to apply the resources to the cluster:
\nkubectl apply -f component.yamlThis will take a few minutes to complete. You can view the progress in the Kubernetes Dashboard we set up earlier.
\nFirst, we can check how the resource utilization in our actual nodes aka Raspberry Pi boards.
\nRun the following command:
\nkubectl top nodesYou might get the output similar to the below:
\nNAME CPU(cores) CPU% MEMORY(bytes) MEMORY% \nmaster 413m 10% 903Mi 24% \nnode-1 206m 5% 648Mi 17% \nnode-2 207m 5% 536Mi 14% \nnode-3 216m 5% 708Mi 19% According to the output above, we are good with the existing resources. :)
\nNow you can check the resource usage of the pods (Memory and CPU as a percentage of the allocated amounts). Run the following command to see them:
\nkubectl top podsNAME CPU(cores) MEMORY(bytes) \ndev-diary-f49dcbd5c-8hsxb 13m 6Mi \ndev-diary-f49dcbd5c-c7j9z 17m 5Mi \ndev-diary-f49dcbd5c-tfh5b 17m 6MiCreate the HorizontalPodAutoscaler:
kubectl autoscale deployment dev-diary --cpu-percent=5 --min=3 --max=10We have a dev-diary called deployment with 3 replicas. So I made the min in HPA to also 3 and max as 10 pods. min CPU percentage is set as 10% to trigger HPA easily.
\nTo test the auto scaler next we need to generate some load. I will be using hey which is a tiny program that sends some load to a web application.
\nNow run the following watch command to continuously check the HPA for dev-diary deployment:
\nkubectl get hpa dev-diary --watch In another terminal, run the following command directed at your application to generate some load:
\nhey -c 1000 -n 2000 https://map-app.comAbove generate 2000 requests from 1000 workers concurrently.
\nNow you might get an output similar to the below:
\nNAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE\ndev-diary Deployment/dev-diary 1%/10% 3 10 3 16s\ndev-diary Deployment/dev-diary 11%/10% 3 10 3 45s\ndev-diary Deployment/dev-diary 16%/10% 3 10 4 60s\ndev-diary Deployment/dev-diary 4%/10% 3 10 5 76s\ndev-diary Deployment/dev-diary 0%/10% 3 10 5 91s\ndev-diary Deployment/dev-diary 0%/10% 3 10 3 6m32sWe can observe that when the target percentage is passed by the load new pods are spun up by the auto scaler to achieve the desired state we declared. Now you might be thinking that's all we have to consider and we can increase the max number of pods as we get more load. But pods are running in our Nodes and all the resources should get utilized at some point. At that point, you need to vertically scale your cluster by adding new Raspberry Pi nodes to our cluster.
\nHope you enjoy the post! Thanks for reading!
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