# Try HTTP autoscaling The following quickstart guide will walk you through installing KEDA + Kedify in a [k3d](https://k3d.io/) Kubernetes cluster and deploying a sample application to demonstrate how Kedify scales an application using HTTP requests. This example uses k3d ingress and a disposable cluster. Other Kubernetes distributions require their own compatibility and routing checks; use the [Helm installation guide](https://docs.kedify.io/installation/helm/) for a managed environment. ## [Prerequisites](https://docs.kedify.io/getting-started/quickstart/#prerequisites) 1. `kubectl` command line utility installed and accessible 2. `helm` command line utility installed and accessible 3. `curl` command line utility installed and accessible 4. [k3d](https://k3d.io/#installation) installed on the machine via ```plaintext brew install k3d ``` or ```plaintext curl -s https://raw.githubusercontent.com/rancher/k3d/main/install.sh | bash ``` or using an [installation method](https://k3d.io/#installation) of your choice. #### Create a new k3d cluster In order to try HTTP based autoscaling with our sample application, we need to create a cluster with properly configured ingress. We can create a k3d cluster with port forwarding to the cluster's load balancer. ```plaintext k3d cluster create --port "9080:80@loadbalancer" ``` ## [KEDA + Kedify Installation](https://docs.kedify.io/getting-started/quickstart/#kedify-installation) Let's install KEDA + Kedify on your cluster! The following three steps will walk you through the basics. Installing the Kedify Agent will automatically install KEDA. ### [Step 1: Install with Helm](https://docs.kedify.io/getting-started/quickstart/#step-1-install-kedify-agent) Enter your details to generate your Helm installation command. After Helm finishes, you should see the Kedify Agent and KEDA installed in your cluster. ```plaintext kubectl get deployment -n keda -w ``` The Kedify Agent will be installed as the first step, then it will configure KEDA with HTTP Add-On in the same namespace and in a minute (once all images are pulled, based on your connection speed), the output should be similar to this: ```plaintext NAME READY UP-TO-DATE AVAILABLE AGE kedify-agent 1/1 1 1 1m keda-add-ons-http-interceptor 1/1 1 1 1m keda-add-ons-http-controller-manager 1/1 1 1 1m keda-add-ons-http-external-scaler 1/1 1 1 1m keda-operator 1/1 1 1 1m keda-operator-metrics-apiserver 1/1 1 1 1m keda-admission-webhooks 1/1 1 1 1m ``` ### [Step 2: Autoscale Application](https://docs.kedify.io/getting-started/quickstart/#step-2-autoscale-application) Run the following commands to deploy a sample application that responds to HTTP requests. ```plaintext kubectl apply -f 'https://dashboard.kedify.io/public/files/sample_http_deployment.yaml' ``` The application is configured with an Ingress and a Service suitable for k3d configured above. If you are using a different Kubernetes distribution, you may need to modify the Ingress configuration. Confirm the application is deployed correctly. ```plaintext kubectl get deployment -n default -w ``` You should see the following output: ```plaintext NAME READY UP-TO-DATE AVAILABLE AGE http-demo-deployment 1/1 1 1 26s ``` #### Test the application is working correctly We are making an HTTP request to the application to confirm it is working correctly. The request is made to the `k3d` load balancer, which is listening on port 9080, we are using the host header to route the request to the correct service. ```plaintext curl -I -H 'host: demo.keda' http://localhost:9080 ``` You should see the following output: ```plaintext HTTP/1.1 200 OK Content-Length: 320 Content-Type: text/html Date: Tue, 23 Jul 2024 22:27:59 GMT ``` #### Add ScaledObject Run the following command to deploy a `ScaledObject` that will scale the application based on the number of HTTP requests. ```plaintext kubectl apply -f 'https://dashboard.kedify.io/public/files/sample_http_scaledobject.yaml' ``` This `ScaledObject` is using Kedify HTTP Trigger. Kedify Build of KEDA is needed to run this properly. #### Confirm the app is scaled to zero Confirm the application has been scaled to zero and that Kedify Proxy is running correctly. The `ScaledObject` defined above tells KEDA to scale down to 0 when there is no HTTP traffic flowing to the application. ```plaintext kubectl get deployment -n default -w ``` You should see the following output, app scaled to zero and Kedify Proxy up and running: ```plaintext NAME READY UP-TO-DATE AVAILABLE AGE http-demo-deployment 0/0 0 0 12m kedify-proxy 1/1 1 1 35s ``` The Kedify Proxy serves as a shared HTTP reverse proxy for all applications in a particular namespace. It is deployed by Kedify Agent only in namespaces that require it for scaling. All `ScaledObjects` that use the HTTP scaler have their traffic routed through an instance of Kedify Proxy and KEDA can scale the applications based on the metrics from the proxy. ### [Step 3: Monitor Autoscaling](https://docs.kedify.io/getting-started/quickstart/#step-3-monitor-autoscaling) Perform another HTTP request to test that the application is correctly scaled out and responds correctly. ```plaintext curl -I -H 'host: demo.keda' http://localhost:9080 ``` You should see the following output: ```plaintext HTTP/1.1 200 OK Content-Length: 320 Content-Type: text/html Date: Tue, 23 Jul 2024 22:39:29 GMT Server: envoy X-Envoy-Upstream-Service-Time: 4103 X-Keda-Http-Cold-Start: true ``` Confirm the application has been scaled out to handle the traffic. ```plaintext kubectl get deployment -n default ``` You should see the following output: ```plaintext NAME READY UP-TO-DATE AVAILABLE AGE kedify-proxy 1/1 1 1 3m9s http-demo-deployment 1/1 1 1 14m ``` By sending more requests you can see that the application is scaled out to a higher number of replicas. You can also use the [Kedify Dashboard](https://dashboard.kedify.io/api/auth/login) to monitor resources, metrics, and more. ![Kedify Dashboard](https://docs.kedify.io/assets/images/how-to/http-scaling-for-ingress-based-applications/step-3-load.png) ## Cleanup Stop request generators and verify the demo returns to its configured idle replica count. The expected first result is a successful request after zero-to-one activation, followed by ready capacity under load. If that fails, inspect [workload scaling](https://docs.kedify.io/troubleshooting/workload-scaling/) before changing thresholds. If you created the disposable default k3d cluster for this tutorial, confirm its name with `k3d cluster list`, then remove only that demo cluster with `k3d cluster delete k3s-default`. This deletes its workloads and data. For an existing cluster, remove only the demo manifests you applied through their owner. Compare [zero and warm capacity](https://docs.kedify.io/guides/scale-to-zero/) before applying the demo to a latency-sensitive service. For another traffic path, use the [HTTP integration chooser](https://docs.kedify.io/scalers/http-scaler/). ## [Next Steps](https://docs.kedify.io/getting-started/quickstart/#next-steps) - Review the [Kedify Key Concepts](https://docs.kedify.io/concepts/kedify-architecture/) - [Choose what to enable next](https://docs.kedify.io/features/) from a measured workload need. ## Related documentation **Choose:**[Scale to zero or keep warm capacity](https://docs.kedify.io/guides/scale-to-zero/) ยท [Choose what to enable](https://docs.kedify.io/features/) **Diagnose:**[Workload does not scale, or scales too slowly](https://docs.kedify.io/troubleshooting/workload-scaling/) **Related capabilities:**[HTTP scaling](https://docs.kedify.io/scalers/http-scaler/) [Next Scaling](https://docs.kedify.io/features/) --- Canonical: https://docs.kedify.io/getting-started/quickstart/ Source: src/pages/getting-started/quickstart.astro Documentation index: https://docs.kedify.io/llms.txt