This guide walks you through setting up Flagger and AWS App Mesh on EKS.

App Mesh

The App Mesh integration with EKS is made out of the following components:

• Kubernetes custom resources

  • mesh.appmesh.k8s.aws defines a logical boundary for network traffic between the services

  • virtualnode.appmesh.k8s.aws defines a logical pointer to a Kubernetes workload

  • virtualservice.appmesh.k8s.aws defines the routing rules for a workload inside the mesh

• CRD controller - keeps the custom resources in sync with the App Mesh control plane

• Admission controller - injects the Envoy sidecar and assigns Kubernetes pods to App Mesh virtual nodes

• Metrics server - Prometheus instance that collects and stores Envoy's metrics

Prerequisites:

• jq

• homebrew

• openssl

• kubectl

• AWS CLI (default region us-west-2)

Create a Kubernetes cluster

In order to create an EKS cluster you can use eksctl. Eksctl is an open source command-line utility made by Weaveworks in collaboration with Amazon, it’s a Kubernetes-native tool written in Go.

On MacOS you can install eksctl with Homebrew:

brew tap weaveworks/tap
brew install weaveworks/tap/eksctl

Create an EKS cluster:

eksctl create cluster --name=appmesh \
--region=us-west-2 \
--appmesh-access

The above command will create a two nodes cluster with App Mesh IAM policy attached to the EKS node instance role.

Verify the install with:

kubectl get nodes

Install Helm

Install the Helm command-line tool:

brew install kubernetes-helm

Create a service account and a cluster role binding for Tiller:

kubectl -n kube-system create sa tiller
​
kubectl create clusterrolebinding tiller-cluster-rule \
--clusterrole=cluster-admin \
--serviceaccount=kube-system:tiller

Deploy Tiller in the kube-system namespace:

helm init --service-account tiller

You should consider using SSL between Helm and Tiller, for more information on securing your Helm installation see docs.helm.sh.

Enable horizontal pod auto-scaling

Install the Horizontal Pod Autoscaler (HPA) metrics provider:

helm upgrade -i metrics-server stable/metrics-server \
--namespace kube-system

After a minute, the metrics API should report CPU and memory usage for pods. You can very the metrics API with:

kubectl -n kube-system top pods

Install the App Mesh components

Run the App Mesh installer:

curl -fsSL https://git.io/get-app-mesh-eks.sh | bash -

The installer does the following:

• creates the appmesh-system namespace

• generates a certificate signed by Kubernetes CA

• registers the App Mesh mutating webhook

• deploys the App Mesh webhook in appmesh-system namespace

• deploys the App Mesh CRDs

• deploys the App Mesh controller in appmesh-system namespace

• creates a mesh called global

Verify that the global mesh is active:

kubectl describe mesh
​
Status:
  Mesh Condition:
    Status:                True
    Type:                  MeshActive

Install Flagger and Grafana

Add Flagger Helm repository:

helm repo add flagger https://flagger.app

Deploy Flagger and Prometheus in the appmesh-system namespace:

helm upgrade -i flagger flagger/flagger \
--namespace=appmesh-system \
--set meshProvider=appmesh \
--set prometheus.install=true

In order to collect the App Mesh metrics that Flagger needs to run the canary analysis, you'll need to setup a Prometheus instance to scrape the Envoy sidecars.

You can enable Slack notifications with:

helm upgrade -i flagger flagger/flagger \
--namespace=appmesh-system \
--set meshProvider=appmesh \
--set metricsServer=http://prometheus.appmesh:9090 \
--set slack.url=https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK \
--set slack.channel=general \
--set slack.user=flagger

Flagger comes with a Grafana dashboard made for monitoring the canary analysis. Deploy Grafana in the appmesh-system namespace:

helm upgrade -i flagger-grafana flagger/grafana \
--namespace=appmesh-system \
--set url=http://flagger-prometheus.appmesh-system:9090

You can access Grafana using port forwarding:

kubectl -n appmesh-system port-forward svc/flagger-grafana 3000:80

Now that you have Flagger running you can try the App Mesh canary deployments tutorial.