KLT End-to-end exercise

This page gives instructions for installing the Keptn Lifecycle Toolkit and running a simple Keptn application to familiarize yourself with how the Keptn Lifecycle Toolkit works and implements full deployment orchestration.

You will learn how to do the following:

• Use the Keptn Lifecycle Toolkit to control the deployment of your application
• Connect the lifecycle-toolkit to Prometheus
• Use pre-deployment tasks to check if a dependency is met before deploying a workload
• Use post-deployment tasks on an application level to send a notification

Prerequisites

To complete this exercise, you need:

• A Kubernetes cluster running Kubernetes 1.24 or later.

• kubectl installed on your system.

You can use an existing cluster or you can create a local cluster to use; see Create local Kubernetes cluster for details.

Run the following and verify that both client and server versions are running Kubernetes versions greater than or equal to v1.24. In this example, both client and server are at v1.24.0 so are appropriate for the Keptn Lifecycle Toolkit.

kubectl version --short

The output should look like the following

Client Version: v1.24.0
Kustomize Version: v4.5.4
Server Version: v1.24.0

Clone the repository for this exercise

We provide a repository that contains the sample application used in this exercise as well as some helper scripts that make it easier for you to set up your environment. Use the following command to clone this repository:

git clone https://github.com/keptn-sandbox/lifecycle-toolkit-examples.git
cd lifecycle-toolkit-examples

Install the required observability features

The Keptn Lifecycle Toolkit emits OpenTelemetry data as standard but the toolkit does not come pre-bundled with observability backend tooling. This is deliberate as it provides flexibility for you to bring your own observability backend that consumes this emitted data.

In order to use the observability features of the lifecycle toolkit, we need a monitoring and tracing backend.

In this guide, we use:

• Prometheus for Metrics
• Jaeger for Traces
• Grafana for Dashboarding

Install these together with the KLT with the following command:

make install

Note To export traces to the OpenTelemetry Collector, you need a KeptnConfig CRD with spec.OTelCollectorUrl specified in the namespace where KLT is installed.

The Demo Application

For this demonstration, we use a slightly modified version of the PodTatoHead application.

Over time, we will evolve this application from a simple manifest to a Keptn-managed application:

• We install it with kubectl then add pre- and post-deployment tasks.
  • For this, we check if the entry service is available before the other services are scheduled.
• We then add evaluations to ensure that our infrastructure is in good shape before we deploy the application.
• Finally, we evolve to a GitOps driven deployment and notify an external webhook service when the deployment has finished.

Install the Demo Application (Version 1)

In the first version of the Demo application, the Keptn Lifecycle Toolkit evaluates metrics provided by Prometheus and checks if the specified amount of CPUs are available before deploying the application

To install it, simply apply the manifest:

make deploy-version-1

You can watch the progress of the deployment as follows:

Watch workload state

When the Lifecycle Toolkit detects workload labels (“app.kubernetes.io/name” or “keptn.sh/workload”) on a resource, a KeptnWorkloadInstance (kwi) resource is created. Using this resource you can watch the progress of the deployment.

kubectl get keptnworkloadinstances -n podtato-kubectl

This shows the current status of the Workloads and in which phase they are at the moment. You can get more detailed information about the workloads by describing one of the resources:

kubectl describe keptnworkloadinstances podtato-head-podtato-head-entry -n podtato-kubectl

Note The event stream of the object contains more detailed information

kubectl get pods -n podtato-kubectl

Furthermore, you can port-forward the podtato-head service to your local machine and access the application via your browser:

make port-forward-grafana

In your browser (http://localhost:3000), log in with the user admin and the password admin). You can open the Dashboard Keptn Applications and see the current state of the application, which should be similar to the following:

In this screen you get the following information:

• Successful/Failed Deployments
• Time between Deployments
• Deployment Time per Version
• The link to the Trace of the deployment

After some time (~60 seconds), you should see one more failed deployment in your dashboard. You can click on the link to the trace and see the reason for the failure:

In this case, we see the name of the failed pre-deployment evaluation and the reason for the failure. In this case, the minimum amount of CPUs is not met. This is a problem we can solve by changing the threshold in the evaluation file.

Install the Demo Application (Version 2)

To achieve this, we changed the operator in the evaluation file (sample-app/version-2/app-pre-deploy-eval) from < to > and applied the new manifest:

kubectl apply -f sample-app/version-2

After this, you can inspect the new state of the application using the same commands as before. You should see that the deployment is now successful and that the trace is also updated. You should also see in the Grafana Dashboards that the deployment was successful.

Congratulations! You have successfully deployed an application using the Keptn Lifecycle Toolkit!