Anthos clusters on bare metal is now Google Distributed Cloud (software only) for bare metal. For more information, see the product overview.

Deploy an application

This document describes how to deploy an application on Google Distributed Cloud.

Before you begin

To deploy a workload, you must have a user, hybrid, or standalone cluster capable of running workloads.

Create a Deployment

The following steps create a Deployment on your cluster:

Copy the following manifest to a file named my-deployment.yaml:

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"

Use kubectl apply to create the Deployment:
```
kubectl apply -f my-deployment.yaml --kubeconfig CLUSTER_KUBECONFIG
```
Replace CLUSTER_KUBECONFIG with the path of the kubeconfig file for your cluster.

Get basic information about your Deployment to confirm it was created successfully:

kubectl get deployment my-deployment --kubeconfig CLUSTER_KUBECONFIG

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 get pods --kubeconfig CLUSTER_KUBECONFIG

The output shows that your Deployment has three running Pods:

NAME                             READY   STATUS    RESTARTS   AGE
my-deployment-869f65669b-5259x   1/1     Running   0          34s
my-deployment-869f65669b-9xfrs   1/1     Running   0          34s
my-deployment-869f65669b-wn4ft   1/1     Running   0          34s

Get detailed information about your Deployment:

kubectl get deployment my-deployment --output yaml --kubeconfig CLUSTER_KUBECONFIG

The output shows details about the Deployment spec and status:

apiVersion: apps/v1
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: "2023-06-29T16:17:17Z"
    lastUpdateTime: "2023-06-29T16:17:17Z"
    message: Deployment has minimum availability.
    reason: MinimumReplicasAvailable
    status: "True"
    type: Available
  - lastTransitionTime: "2023-06-29T16:17:12Z"
    lastUpdateTime: "2023-06-29T16:17:17Z"
    message: ReplicaSet "my-deployment-869f65669b" has successfully progressed.
    reason: NewReplicaSetAvailable
    status: "True"
    type: Progressing
  observedGeneration: 1
  readyReplicas: 3
  replicas: 3
  updatedReplicas: 3

Describe your Deployment:

kubectl describe deployment my-deployment --kubeconfig CLUSTER_KUBECONFIG

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

Name:                   my-deployment
Namespace:              default
CreationTimestamp:      Thu, 29 Jun 2023 16:17:12 +0000
Labels:                 <none>
Annotations:            deployment.kubernetes.io/revision: 1
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:         <none>
    Host Port:    <none>
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>
Conditions:
  Type           Status  Reason
  ----           ------  ------
  Available      True    MinimumReplicasAvailable
  Progressing    True    NewReplicaSetAvailable
OldReplicaSets:  <none>
NewReplicaSet:   my-deployment-869f65669b (3/3 replicas created)
Events:
  Type    Reason             Age    From                   Message
  ----    ------             ----   ----                   -------
  Normal  ScalingReplicaSet  6m50s  deployment-controller  Scaled up replica set my-deployment-869f65669b to 3

Create 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.

To create a Service of type LoadBalancer:

Copy the following manifest to a file named my-service.yaml:
```
apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: metrics
    department: sales
  type: LoadBalancer
  ports:
  - port: 80
    targetPort: 8080
```
Here are the important things to understand about the Service in this exercise:
- Any Pod that has the label app: metrics and the label department: sales is a member of the Service. The Pods in my-deployment have these labels.
- When a client sends a request to the Service on TCP port80, 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.
By default, the hello-app container listens on TCP port 8080. You can see this port setting by looking at the Dockerfile and the source code for the app.
Use kubectl apply to create the Service on your cluster:
```
kubectl apply -f my-service.yaml --kubeconfig CLUSTER_KUBECONFIG
```
Replace CLUSTER_KUBECONFIG with the path of the kubeconfig file for your cluster.
View your Service:
```
kubectl get service my-service --output yaml --kubeconfig CLUSTER_KUBECONFIG
```
The output is similar to the following:
```
apiVersion: v1
kind: Service
metadata:
  ...
  name: my-service
  namespace: default
  ...
spec:
  allocateLoadBalancerNodePorts: true
  clusterIP: 10.96.2.165
  clusterIPs:
  - 10.96.2.165
  externalTrafficPolicy: Cluster
  internalTrafficPolicy: Cluster
  ipFamilies:
  - IPv4
  ipFamilyPolicy: SingleStack
  ports:
  - nodePort: 31565
    port: 80
    protocol: TCP
    targetPort: 8080
  selector:
    app: metrics
    department: sales
  sessionAffinity: None
  type: LoadBalancer
status:
  loadBalancer:
    ingress:
    - ip: 192.168.1.13
```
In the preceding output, you can see that your Service has a clusterIP, and an external IP address. It also has a nodePort, a port, and a targetPort.

The clusterIP isn't relevant to this exercise. The external IP address (status.loadBalancer.ingress.ip) comes from the range of addresses that you specified when you defined load balancer address pools (spec.loadBalancer.addressPools) in the cluster configuration file.

As an example, take the values shown in the preceding output for your Service:
- External IP address: 192.168.1.13
- port: 80
- nodePort: 31565
- targetPort: 8080
A client sends a request to 192.168.1.13 on TCP port 80. The request is routed to your load balancer, and from there it is forwarded to a member Pod on TCP port 8080.
Call your Service:
```
curl INGRESS_IP_ADDRESS
```
Replace INGRESS_IP_ADDRESS with the ingress IP address in the status section of the Service that you retrieved in the preceding step (status.loadBalancer.ingress).

The output shows a Hello, world! message:
```
Hello, world!
Version: 2.0.0
Hostname: my-deployment-869f65669b-wn4ft
```

LoadBalancer port limits

The LoadBalancer type is an extension of the NodePort type. So a Service of type LoadBalancer has a cluster IP address and one or more nodePort values. By default, Kubernetes allocates node ports to Services of type LoadBalancer. These allocations can quickly exhaust available node ports from the 2,768 allotted to your cluster. To save node ports, disable load balancer node port allocation by setting the allocateLoadBalancerNodePorts field to false in the LoadBalancer Service spec. This setting prevents Kubernetes from allocating node ports to LoadBalancer Services. For more information, see Disabling load balancer NodePort allocation in the Kubernetes documentation.

Here's a manifest to create a Service that doesn't use any node ports:

apiVersion: v1
kind: Service
metadata:
  name: service-does-not-use-nodeports
spec:
  selector:
    app: my-app
  type: LoadBalancer
  ports:
  - port: 8000
  # Set allocateLoadBalancerNodePorts to false
  allocateLoadBalancerNodePorts: false

Delete your Service

To delete your Service:

Use kubectl delete to delete your Service from your cluster:

kubectl delete service my-service --kubeconfig CLUSTER_KUBECONFIG

Verify that your Service has been deleted:
```
kubectl get services --kubeconfig CLUSTER_KUBECONFIG
```
The output no longer shows my-service.

Delete your Deployment

To delete your Deployment:

Use kubectl delete to delete your Deployment from your cluster:
```
kubectl delete deployment my-deployment --kubeconfig CLUSTER_KUBECONFIG
```
Verify that your Deployment has been deleted:
```
kubectl get deployments --kubeconfig CLUSTER_KUBECONFIG
```
The output no longer shows my-deployment.

What's next

Create a Service and an Ingress