Traefik

Intro

Traefik is a leading modern reverse proxy and load balancer that makes deploying microservices easy. Traefik integrates with your existing infrastructure components and configures itself automatically and dynamically.

Traefik is designed to be as simple as possible to operate, but capable of handling large, highly-complex deployments across a wide range of environments and protocols in public, private, and hybrid clouds. It also comes with a powerful set of middlewares that enhance its capabilities to include load balancing, API gateway, orchestrator ingress, as well as east-west service communication and more.

This recipe describes how customers can standardize the configuration, deployment and lifecycle management of Traefik as a custom system add-on across their fleet of clusters.

What Will You Do¶

In this exercise,

You will create a customized Traefik add-on using Traefik's official Helm chart
You will use the add-on in a custom cluster blueprint
You will then apply this cluster blueprint to a managed cluster
You will then deploy a test workload to use the Traefik ingress controller

Assumptions¶

You have already provisioned or imported one or more EKS clusters using the controller.

Step 1: Create Custom Values File¶

In this step, you will create a custom values file for the Traefik Helm chart. This values file will configure Traefik to use the AWS loadbalancer.

Save the below YAML to a file named traefik-values.yaml

service:
  annotations:
    service.beta.kubernetes.io/aws-load-balancer-type: nlb

Step 2: Create Namesapce¶

You will now create a managed namespace from the controller for the Traefik resources.

Ensure you are logged into the console
In your project, navigate to Infrastructure -> Namespaces
Click on New Namespace
Enter traefik for the name
Select Wizard for Type
Click Save

Create Namespace

Click "Save & Go To Placement"
Select one of your clusters for placement
Click "Save & Go To Publish"
Click "Publish"
Click "Exit"

Step 3: Create Addon¶

In this step, you will create a custom cluster add-on from the Catalog. The add-on will then be used in a custom cluster blueprint.

In the Rafay console, navigate to Catalog in the left side tree
Search for "traefik"
Click on traefik

Create Add-On

Click Create Add-On
Enter traefik for the name of the add-on
Select the previously created namespace
Click Create

Create Add-On

Enter v1 for the version name
Click Upload Files under the Values File(s) section
Select the previously saved traefik-values.yaml file
Click Save Changes

Create Add-On

Step 4: Create Bluprint¶

In this step, you will create a custom cluster blueprint which contains the previously created addon.

In the Rafay console, navigate to Infrastructure -> Blueprints in the left side tree
Click New Blueprint
Enter traefik for the name of the blueprint
Click Save

Create Blueprint

Enter v1 for the version name
Select minimal for the base blueprint

Create Blueprint

Click Configure Add-Ons
Click the + symbol to add the traefik add-on to the blueprint
Click Save Changes

Create Blueprint

Click Save Changes

Step 5: Apply Blueprint¶

In this step, you will apply the previously created blueprint to the cluster. Applying the blueprint will install the Traefik ingress controller.

In the Rafay console, navigate to Infrastructure -> Clusters in the left side tree
Click the gear icon on your cluster
Select Update Blueprint
Select traefik for the blueprint and v1 for the version
Click Save and Publish

Apply Blueprint

The blueprint will begin being applied to the cluster

Apply Blueprint

Afer a short period of time, the blueprint will be applied to the cluster.

Apply Blueprint

Click Exit

Step 6: Deploy Workload Resources¶

In this step, you will deploy the resources needed for a test workload that will use the Traefik ingress controller.

Save the following YAML to a file named traefik-workload.yaml

kind: Deployment
apiVersion: apps/v1
metadata:
  name: whoami
  labels:
    app: whoami

spec:
  replicas: 1
  selector:
    matchLabels:
      app: whoami
  template:
    metadata:
      labels:
        app: whoami
    spec:
      containers:
        - name: whoami
          image: traefik/whoami
          ports:
            - name: web
              containerPort: 80
---
apiVersion: v1
kind: Service
metadata:
  name: whoami

spec:
  ports:
    - name: web
      port: 80
      targetPort: web

  selector:
    app: whoami
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: whoami-ingress
spec:
  rules:
  - http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: whoami
            port:
              name: web

In the Rafay console, navigate to Applications -> Workloads in the left side tree
Click New Workload -> Create New Workload
Enter traefik-workload for the name
Select K8s YAML for the package type
Select Upload files manually
Select traefik for the namespace
Click Continue

Create workload

Click Choose File
Select the previously saved traefik-workload.yaml file
Click Save and Go To Placement
Select the cluster to deploy the workload to
Click Save and Go To Publish
Click Publish to deploy the workload

Create workload

Click Exit

Step 7: Verify Workload¶

In this step, you will verify that the previously deployed workload is running and can be accessed through the Traefik ingress.

In the Rafay console, navigate to Infrastructure -> Clusters in the left side tree
Click kubectl on your cluster
Enter the following command:

kubectl get services -n traefik

You will see something similar to the following screenshot.

Verify Workload

Copy the EXTERNAL-IP of the service named traefik
Enter the EXTERNAL-IP into a web browswer (Be sure to use http:// and not https://) to access the test workload through the Traefik ingress.

You will see something similar to the following screenshot showing access to the test workload, whoami.

Verify Workload

Recap¶

Congratulations! You have successfully created a custom cluster blueprint with the Traefik ingress controller as a custom add-on. You can now use this blueprint on as many clusters as you require.