cdk8s is a great way to bundle and compose applications to be deployed to Kubernetes.

cdk8s uses a concept of an APIObject to create K8s api-resources in a programming language and for now this is Typescript and Python with more coming. Each APIObject is created in the context of a Construct and the constructs are composed together into a Chart. The chart concept is ofcourse familiar and analogous to helm charts- kind of.

An example to help make sense of this:

In Typescript you can declare a Construct that creates a Service APIObject/resource as follows:

class MyConstruct extends Construct {
    constructor(scope:Construct, name:string) {
        super(scope, name)
        
        new Service(this, 'service', {
        spec: {
            type: 'LoadBalancer',
            ports: [{ port: 80, targetPort: IntOrString.fromNumber(8080) }],
            selector: label
        }});
    }
}

From here MyConstruct can be instantiated in a Chart together with other Constructs that create their own resources.

To demonstrate composing the Constructs into a chart we can use say, cdk8s-xray and cdk8s-redis, Constructs for AWS X-Ray and Redis:

You’d install cdk8s-redis and cdk8s-xray from NPM

import {Redis} from 'cdk8s-redis'
import { XRayApp, DaemonProtocol } from 'cdk8s-xray'

// inside your chart:
// add application constructs for X-Ray and Redis to chart

let xrayconfig = {
    image: "rnzdocker1/eks-workshop-x-ray-daemon:dbada4c77e6ae10ecf5a7b1c5864aa6522d9fb02",
    ns: "default",
    daemon: {
        daemonProtocol: DaemonProtocol.UDP,
        port: 2000,
        logLevel: "prod"
    }
}

new XRayApp(this, 'prod', xrayconfig);
new Redis(this, "redis", {
    labels:{
        "app":"redis"
    },
    slaveReplicas: 1
});

In the example, two separates Constructs, XRayApp and Redis are combined into a chart. Running cdk8s synth from the cdk8s-cli would generate a manifest with the Kubernetes api-resources defined in each Construct.

The advantages of the cdk8s approach include:

• Reusability and Composability of multiple Constructs into Charts that can be deployed to Kubernetes.
• Testing: As this is all just Python or Typescript you can add unit-tests to say check if every container in a DaemoneSet has resource limits defined- not easily done with plain YAML.
• (GitOps): If your’re passing all this through a CI/CD workflow it can give some control over what get’s deployed.

It’s a lot easier perhaps taking a look at a sample e.g. the Github repo for cdk8s-xray (because I wrote it 👹). There are other great examples here as well.

I hope to see cdk8s develop more- It’s still early days but the potential is all there.