Deploying an application

This document shows how to deploy an application in your user cluster for Google Distributed Cloud.

Before you begin

Create a user cluster (quickstart | full instructions).

SSH into your admin workstation

SSH into your admin workstation:

ssh -i ~/.ssh/vsphere_workstation ubuntu@[IP_ADDRESS]

where [IP_ADDRESS] is the IP address of your admin workstation.

Do all of the remaining steps in this topic on your admin workstation.

Creating a Deployment

Here is a manifest for a Deployment:

 apiVersion: apps/v1 kind: Deployment metadata: name: my-deployment spec: selector: matchLabels: app: metrics department: sales replicas: 3 template: metadata: labels: app: metrics department: sales spec: containers: - name: hello image: "us-docker.pkg.dev/google-samples/containers/gke/hello-app:2.0"

Copy the manifest to a file named my-deployment.yaml, and create the Deployment:

kubectl apply --kubeconfig [USER_CLUSTER_KUBECONFIG] -f my-deployment.yaml

where [USER_CLUSTER_KUBECONFIG] is the path of the kubeconfig file for your user cluster.

Get basic information about your Deployment:

kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] get deployment my-deployment

The output shows that the Deployment has three Pods that are all available:

 NAME READY UP-TO-DATE AVAILABLE AGE my-deployment 3/3 3 3 27s

List the Pods in your Deployment:

kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] get pods

The output shows that your Deployment has three running Pods:

NAME READY STATUS RESTARTS AGE my-deployment-54944c8d55-4srm2 1/1 Running 0 6s my-deployment-54944c8d55-7z5nn 1/1 Running 0 6s my-deployment-54944c8d55-j62n9 1/1 Running 0 6s

Get detailed information about your Deployment:

kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] get deployment my-deployment --output yaml

The output shows details about the Deployment spec and status:

 kind: Deployment metadata: ... generation: 1 name: my-deployment namespace: default ... spec: ... replicas: 3 revisionHistoryLimit: 10 selector: matchLabels: app: metrics department: sales ... spec: containers: - image: us-docker.pkg.dev/google-samples/containers/gke/hello-app:2.0 imagePullPolicy: IfNotPresent name: hello resources: {} terminationMessagePath: /dev/termination-log terminationMessagePolicy: File dnsPolicy: ClusterFirst restartPolicy: Always schedulerName: default-scheduler securityContext: {} terminationGracePeriodSeconds: 30 status: availableReplicas: 3 conditions: - lastTransitionTime: "2019-11-11T18:44:02Z" lastUpdateTime: "2019-11-11T18:44:02Z" message: Deployment has minimum availability. reason: MinimumReplicasAvailable status: "True" type: Available - lastTransitionTime: "2019-11-11T18:43:58Z" lastUpdateTime: "2019-11-11T18:44:02Z" message: ReplicaSet "my-deployment-54944c8d55" has successfully progressed. reason: NewReplicaSetAvailable status: "True" type: Progressing observedGeneration: 1 readyReplicas: 3 replicas: 3 updatedReplicas: 3

Describe your Deployment:

kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] describe deployment my-deployment

The output shows nicely formatted details about the Deployment, including the associated ReplicaSet:

 Name: my-deployment Namespace: default CreationTimestamp: Mon, 11 Nov 2019 10:43:58 -0800 Labels:  ... Selector: app=metrics,department=sales Replicas: 3 desired | 3 updated | 3 total | 3 available | 0 unavailable StrategyType: RollingUpdate MinReadySeconds: 0 RollingUpdateStrategy: 25% max unavailable, 25% max surge Pod Template: Labels: app=metrics department=sales Containers: hello: Image: us-docker.pkg.dev/google-samples/containers/gke/hello-app:2.0 Port:  Host Port:  Environment:  Mounts:  Volumes:  Conditions: Type Status Reason ---- ------ ------ Available True MinimumReplicasAvailable Progressing True NewReplicaSetAvailable OldReplicaSets:  NewReplicaSet: my-deployment-54944c8d55 (3/3 replicas created)

Creating a Service of type LoadBalancer

One way to expose your Deployment to clients outside your cluster is to create a Kubernetes Service of type LoadBalancer.

Here's a manifest for a Service of type LoadBalancer:

 apiVersion: v1 kind: Service metadata: name: my-service spec: selector: app: metrics department: sales type: LoadBalancer loadBalancerIP: [SERVICE_IP_ADDRESS] ports: - port: 80 targetPort: 8080

For the purpose of this exercise, these are the important things to understand about the Service:

  • Any Pod that has the label app: metrics and the label department: sales is a member of the Service. Note that the Pods in my-deployment have these labels.

  • When a client sends a request to the Service on TCP port 80, the request is forwarded to a member Pod on TCP port 8080.

  • Every member Pod must have a container that is listening on TCP port 8080.

It happens that by default, the hello-app container listens on TCP port 8080. You can see this by looking at the Dockerfile and the source code for the app.

Replace [SERVICE_IP_ADDRESS] with an address that you own that is not already in use. For example, you could set this to a public IP address that your company owns. Or you could set it to a private address in your company network.

The address you choose must be routable from the location of any client that sends requests to the Service. For example, if you choose a private address, then external clients will not be able to send requests to the Service.

Save the manifest to a file named my-service.yaml, and create the Service:

 kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] apply -f my-service.yaml

where [USER_CLUSTER_KUBECONFIG] is the path of the kubeconfig file for your user cluster.

When you create the Service, Google Distributed Cloud automatically configures the loadBalancerIP address on your F5 BIG-IP load balancer.

View your Service:

 kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] get service my-service --output yaml

The output is similar to this:

 apiVersion: v1 kind: Service metadata: ... name: my-service namespace: default ... spec: clusterIP: 10.107.84.202 externalTrafficPolicy: Cluster loadBalancerIP: 203.0.113.1 ports: - nodePort: 31919 port: 80 protocol: TCP targetPort: 8080 selector: app: metrics department: sales sessionAffinity: None type: LoadBalancer status: loadBalancer: ingress: - ip: 203.0.113.1

In the preceding output, you can see that your Service has a clusterIP, and a loadBalancerIP. It also has a nodePort, a port, and a targetPort.

The clusterIP is not relevant to this exercise. The loadBalancerIP is the IP address that you provided in my-service.yaml.

As an example, take the values shown in the preceding output. That is, suppose your Service has loadBalancerIP = 203.0.113.1, port = 80, nodePort = 31919, and targetPort = 8080.

A client sends a request to 203.0.113.1 on TCP port 80. The request gets routed to your F5 BIG-IP load balancer. The load balancer chooses one of your user cluster nodes, and forwards the request to [NODE_ADDRESS] on TCP port 31919. The iptables rules on the node forward the request to a member Pod on TCP port 8080.

Call your Service:

curl [SERVICE_IP_ADDRESS]

where [SERVICE_IP_ADDRESS] is the address that you specified for loadBalancerIP.

The output shows a Hello, world! message:

 curl 21.0.133.48 Hello, world! Version: 2.0.0 Hostname: my-deployment-dbd86c8c4-9wpbv

Deleting your Service

Delete your Service:

kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] delete service my-service

Verify that your Service has been deleted:

kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] get services

The output no longer shows my-service.

Deleting your Deployment

Delete your Deployment:

kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] delete deployment my-deployment

Verify that your Deployment has been deleted:

kubectl --kubeconfig [USER_CLUSTER_KUBECONFIG] get deployments

The output no longer shows my-deployment.

What's next

Create a Service and an Ingress