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https://github.com/learnk8s/spring-boot-k8s-hpa
Autoscaling Spring Boot with the Horizontal Pod Autoscaler and custom metrics on Kubernetes
https://github.com/learnk8s/spring-boot-k8s-hpa
autoscaling docker-container kubernetes spring-boot
Last synced: 1 day ago
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Autoscaling Spring Boot with the Horizontal Pod Autoscaler and custom metrics on Kubernetes
- Host: GitHub
- URL: https://github.com/learnk8s/spring-boot-k8s-hpa
- Owner: learnk8s
- Created: 2018-05-17T18:04:55.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-03-31T18:43:24.000Z (almost 3 years ago)
- Last Synced: 2024-12-23T19:13:10.673Z (8 days ago)
- Topics: autoscaling, docker-container, kubernetes, spring-boot
- Language: Java
- Homepage: https://learnk8s.io/blog/scaling-spring-boot-microservices
- Size: 61.5 KB
- Stars: 322
- Watchers: 34
- Forks: 136
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# Autoscaling Spring Boot with the Horizontal Pod Autoscaler and custom metrics on Kubernetes
## Prerequisites
You should have minikube installed.
You should start minikube with at least 4GB of RAM:
```bash
minikube start \
--memory 4096 \
--extra-config=controller-manager.horizontal-pod-autoscaler-upscale-delay=1m \
--extra-config=controller-manager.horizontal-pod-autoscaler-downscale-delay=2m \
--extra-config=controller-manager.horizontal-pod-autoscaler-sync-period=10s
```
> If you're using a pre-existing minikube instance, you can resize the VM by destroying it an recreating it. Just adding the `--memory 4096` won't have any effect.
You should install `jq` — a lightweight and flexible command-line JSON processor.
You can find more [info about `jq` on the official website](https://github.com/stedolan/jq).
## Installing Custom Metrics Api
Deploy the Metrics Server in the `kube-system` namespace:
```bash
kubectl create -f monitoring/metrics-server
```
After one minute the metric-server starts reporting CPU and memory usage for nodes and pods.
View nodes metrics:
```bash
kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes" | jq .
```
View pods metrics:
```bash
kubectl get --raw "/apis/metrics.k8s.io/v1beta1/pods" | jq .
```
Create the monitoring namespace:
```bash
kubectl create -f monitoring/namespaces.yaml
```
Deploy Prometheus v2 in the monitoring namespace:
```bash
kubectl create -f monitoring/prometheus
```
Deploy the Prometheus custom metrics API adapter:
```bash
kubectl create -f monitoring/custom-metrics-api
```
List the custom metrics provided by Prometheus:
```bash
kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1" | jq .
```
## Package the application
You package the application as a container with:
```bash
eval $(minikube docker-env)
docker build -t spring-boot-hpa .
```
## Deploying the application
Deploy the application in Kubernetes with:
```bash
kubectl create -f kube/deployment
```
You can visit the application at http://minkube_ip:32000
> (Find the minikube ip address via `minikube ip`)
You can post messages to the queue by via http://minkube_ip:32000/submit?quantity=2
You should be able to see the number of pending messages from http://minkube_ip:32000/metrics and from the custom metrics endpoint:
```bash
kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta1/namespaces/default/pods/*/messages" | jq .
```
## Autoscaling workers
You can scale the application in proportion to the number of messages in the queue with the Horizontal Pod Autoscaler. You can deploy the HPA with:
```bash
kubectl create -f kube/hpa.yaml
```
You can send more traffic to the application with:
```bash
while true; do curl -d "quantity=1" -X POST http://minkube_ip:32000/submit ; sleep 4; done
```
When the application can't cope with the number of incoming messages, the autoscaler increases the number of pods in 3 minute intervals.
You may need to wait three minutes before you can see more pods joining the deployment with:
```bash
kubectl get pods
```
The autoscaler will remove pods from the deployment every 5 minutes.
You can inspect the event and triggers in the HPA with:
```bash
kubectl get hpa spring-boot-hpa
```
## Notes
The configuration for metrics and metrics server is configured to run on minikube only.
**You won't be able to run the same YAML files for metrics and custom metrics server on your cluster or EKS, GKE, AKS, etc.**
Also, there are secrets checked in the repository to deploy the Prometheus adapter.
**In production, you should generate your own secrets and (possibly) not check them into version control.**
If you wish to run metrics and custom metrics server in production, you should check out the following resources:
- [Metrics server](https://github.com/kubernetes-sigs/metrics-server)
- [How to install Prometheus and the Promtheus Adapter](https://github.com/DirectXMan12/k8s-prometheus-adapter/blob/master/docs/walkthrough.md)