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Stateless Applications

1: Deploy a Release Using a Canary Deployment
2: Exposing an External IP Address to Access an Application in a Cluster
3: Example: Deploying PHP Guestbook application with Redis

1 - Deploy a Release Using a Canary Deployment

This tutorial walks you through deploying a canary release in Kubernetes. A canary deployment allows you to safely test a new application version with a small portion of production traffic before rolling it out completely. This approach helps minimize risk and enables rapid feedback from real users.

For an overview of canary deployments, see Canary deployments in the Managing Workloads concept page.

Objectives

Deploy a stable version of an application
Deploy a canary version alongside the stable version
Route traffic to both versions using a Service
Monitor the canary deployment
Complete the rollout by scaling the canary and removing the stable version

Before you begin

You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:

Understanding canary deployments

A canary deployment is a strategy where you deploy a new version of your application alongside the existing version. The new version (canary) receives a small percentage of traffic, allowing you to:

Test the new version with real production traffic
Monitor for errors, performance issues, or unexpected behavior
Roll back quickly if issues are detected
Gradually increase traffic to the new version if it performs well

In this tutorial, you'll use the track label to differentiate between the stable and canary releases. The stable release uses track: stable, and the canary release uses track: canary. Both deployments share common labels (app.kubernetes.io/name: rollout-demo) that allow the Service to route traffic to both sets of Pods.

You'll deploy the stable version with 3 replicas and the canary version with 1 replica. Since both versions share the same Service selector (app.kubernetes.io/name: rollout-demo), Kubernetes will load-balance traffic across all 4 pods. With this ratio, approximately 25% of traffic goes to the canary and 75% to the stable version.

Deploying the stable version

First, deploy the stable version of your application:

application/canary/app-v1-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: rollout-demo-stable
  labels:
    app.kubernetes.io/name: rollout-demo
    track: stable
spec:
  replicas: 3
  selector:
    matchLabels:
      app.kubernetes.io/name: rollout-demo
      track: stable
  template:
    metadata:
      labels:
        app.kubernetes.io/name: rollout-demo
        track: stable
    spec:
      containers:
      - name: rollout-demo
        image: gcr.io/google-samples/hello-app:1.0
        ports:
        - containerPort: 8080
        env:
        - name: VERSION
          value: "v1"

Apply the stable Deployment:

kubectl apply -f https://k8s.io/examples/application/canary/app-v1-deployment.yaml

Verify that the Deployment was created and the Pods are running:

kubectl get deployments -l app.kubernetes.io/name=rollout-demo

The output is similar to:

NAME                  READY   UP-TO-DATE   AVAILABLE   AGE
rollout-demo-stable    3/3     3            3           10s

Check the Pods:

kubectl get pods -l app.kubernetes.io/name=rollout-demo

The output is similar to:

NAME                                  READY   STATUS    RESTARTS   AGE
rollout-demo-stable-7d4b9b8c5d-abc12   1/1     Running   0          15s
rollout-demo-stable-7d4b9b8c5d-def34   1/1     Running   0          15s
rollout-demo-stable-7d4b9b8c5d-ghi56   1/1     Running   0          15s

Creating a service

Create a Service to expose your application. The Service selector uses the common label (app.kubernetes.io/name: rollout-demo) and omits the track label, which allows it to route traffic to both the stable and canary Pods:

application/canary/app-service.yaml

apiVersion: v1
kind: Service
metadata:
  name: rollout-demo-service
  labels:
    app.kubernetes.io/name: rollout-demo
spec:
  type: ClusterIP
  selector:
    app.kubernetes.io/name: rollout-demo
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080

Apply the Service:

kubectl apply -f https://k8s.io/examples/application/canary/app-service.yaml

Verify the Service was created:

kubectl get service rollout-demo-service

The output is similar to:

NAME                  TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE
rollout-demo-service   ClusterIP   10.96.123.45    <none>        80/TCP    5s

Test the Service by creating a temporary Pod and making a request:

kubectl run curl-test --image=curlimages/curl:latest --rm -it --restart=Never -- curl http://rollout-demo-service

You should see responses from the stable Pods. Run this command multiple times to see different Pod hostnames, but all responses should show version v1.

At this point, your application is in a steady state. In a real-world scenario, you would typically pause here and operate the stable version until you have a new version to deploy. The next steps demonstrate how to introduce a canary version.

Deploying the canary version

To create a canary Deployment, you can copy the stable Deployment manifest and make a few changes:

Change the metadata.name (for example, to rollout-demo-canary)
Change the track label from stable to canary in both metadata.labels and spec.selector.matchLabels/spec.template.metadata.labels
Set a lower number of replicas (for example, 1)
Update the container image to the new version

Now deploy the canary version alongside the stable version:

application/canary/app-v2-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: rollout-demo-canary
  labels:
    app.kubernetes.io/name: rollout-demo
    track: canary
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/name: rollout-demo
      track: canary
  template:
    metadata:
      labels:
        app.kubernetes.io/name: rollout-demo
        track: canary
    spec:
      containers:
      - name: rollout-demo
        image: gcr.io/google-samples/hello-app:2.0
        ports:
        - containerPort: 8080
        env:
        - name: VERSION
          value: "v2"

Apply the canary Deployment:

kubectl apply -f https://k8s.io/examples/application/canary/app-v2-deployment.yaml

Verify both Deployments are running:

kubectl get deployments -l app.kubernetes.io/name=rollout-demo

The output is similar to:

NAME                  READY   UP-TO-DATE   AVAILABLE   AGE
rollout-demo-stable    3/3     3            3           2m
rollout-demo-canary    1/1     1            1           10s

Check all Pods:

kubectl get pods -l app.kubernetes.io/name=rollout-demo -o wide

The output is similar to:

NAME                                  READY   STATUS    RESTARTS   AGE   IP           NODE
rollout-demo-stable-7d4b9b8c5d-abc12   1/1     Running   0          2m    10.244.1.5   node1
rollout-demo-stable-7d4b9b8c5d-def34   1/1     Running   0          2m    10.244.1.6   node1
rollout-demo-stable-7d4b9b8c5d-ghi56   1/1     Running   0          2m    10.244.2.7   node2
rollout-demo-canary-8e5c0d9f6a-xyz78   1/1     Running   0          15s   10.244.2.8   node2

Notice that you now have 4 Pods total: 3 stable Pods and 1 canary Pod.

Testing traffic distribution

Since both Deployments use the same Service selector (app.kubernetes.io/name: rollout-demo), the Service routes traffic to all Pods. With 3 stable Pods and 1 canary Pod, approximately 25% of requests go to the canary.

Test the Service multiple times to see traffic distribution:

kubectl run curl-test --image=curlimages/curl:latest --rm -it --restart=Never -- \
  sh -c 'for i in $(seq 1 10); do curl -s http://rollout-demo-service; echo; done'

You should see responses from both stable and canary versions. The ratio may vary, but you should see some canary responses mixed with stable responses.

Check the Service EndpointSlices to verify both versions are receiving traffic:

kubectl get endpointslices -l kubernetes.io/service-name=rollout-demo-service

The output is similar to (one EndpointSlice per address family; the ENDPOINTS column is truncated by default):

NAME                          ADDRESSTYPE   PORTS   ENDPOINTS                              AGE
rollout-demo-service-abc12    IPv4          8080    10.244.1.5,10.244.1.6,10.244.2.7 + 1 more...   3m

To see all endpoint addresses, use -o yaml.

Monitoring the canary deployment

Monitor your canary deployment for errors, performance issues, or unexpected behavior:

Check Pod logs for the canary:

kubectl logs -l app.kubernetes.io/name=rollout-demo,track=canary --tail=50

Monitor Pod status:

kubectl get pods -l app.kubernetes.io/name=rollout-demo -w

Press Ctrl+C to stop watching.

Check resource usage:

kubectl top pods -l app.kubernetes.io/name=rollout-demo

Note:

The kubectl top command requires the metrics-server to be installed in your cluster. If it's not available, you can monitor using other methods like Prometheus or cloud provider monitoring tools.

Adjusting traffic distribution

If the canary is performing well, you can gradually increase traffic to it by scaling it up:

Scale the canary to 2 replicas (now 40% of traffic):

kubectl scale deployment/rollout-demo-canary --replicas=2

Verify the new Pod is running:

kubectl get pods -l app.kubernetes.io/name=rollout-demo

Continue monitoring. If everything looks good, you can scale the canary further and scale down the stable version.

Completing the rollout

If your monitoring instead uncovered issues with the canary, skip ahead to Rolling back a canary deployment.

Once you're confident that the canary is stable and performing well, complete the rollout:

Scale the canary to the desired number of replicas (e.g., 3):

kubectl scale deployment/rollout-demo-canary --replicas=3

Scale down the stable version to 0:

kubectl scale deployment/rollout-demo-stable --replicas=0

Verify all traffic is going to the canary:

kubectl get pods -l app.kubernetes.io/name=rollout-demo

The output should show only canary Pods:

NAME                                  READY   STATUS    RESTARTS   AGE
rollout-demo-canary-8e5c0d9f6a-xyz78   1/1     Running   0          5m
rollout-demo-canary-8e5c0d9f6a-abc12   1/1     Running   0          2m
rollout-demo-canary-8e5c0d9f6a-def34   1/1     Running   0          2m

Test the Service to confirm all responses are from the canary:

kubectl run curl-test --image=curlimages/curl:latest --rm -it --restart=Never -- curl http://rollout-demo-service

All responses should now show version v2.

To complete the rollout with a single Deployment, first scale the stable Deployment back up so you do not lose capacity if the new image fails to pull. Then update the image and the VERSION environment variable to match the new version, wait for the rollout to complete, and remove the canary Deployment:

kubectl scale deployment/rollout-demo-stable --replicas=3
kubectl set image deployment/rollout-demo-stable rollout-demo=gcr.io/google-samples/hello-app:2.0
kubectl set env deployment/rollout-demo-stable VERSION=v2
kubectl rollout status deployment/rollout-demo-stable
kubectl delete deployment rollout-demo-canary

Rolling back a canary deployment

If you detect issues with the canary version, you can quickly roll back:

Scale down the canary deployment:

kubectl scale deployment/rollout-demo-canary --replicas=0

Scaling to zero preserves the canary Deployment so you can inspect its configuration while you investigate. Once you've finished, delete it with kubectl delete deployment rollout-demo-canary.

Scale the stable version back up if needed:

kubectl scale deployment/rollout-demo-stable --replicas=3

Investigate the issues with the canary before attempting another canary deployment.

Splitting traffic using HTTPRoute (optional)

If you're using the Gateway API, you can use HTTPRoute to have more precise control over traffic splitting between the stable and canary versions. This approach allows you to specify exact percentages for traffic distribution rather than relying on replica counts.

First, create separate Services for the stable and canary versions. This approach is also useful for debugging, even if you are not using Gateway API. By having separate Services, you can directly test or monitor each version independently.

apiVersion: v1
kind: Service
metadata:
  name: rollout-demo-stable-service
spec:
  selector:
    app.kubernetes.io/name: rollout-demo
    track: stable
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080
---
apiVersion: v1
kind: Service
metadata:
  name: rollout-demo-canary-service
spec:
  selector:
    app.kubernetes.io/name: rollout-demo
    track: canary
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080

Then create an HTTPRoute that splits traffic between the two Services:

apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: rollout-demo-route
spec:
  parentRefs:
  - name: example-gateway
  hostnames:
  - "rollout-demo.example.com"
  rules:
  - backendRefs:
    - name: rollout-demo-stable-service
      port: 80
      weight: 90
    - name: rollout-demo-canary-service
      port: 80
      weight: 10

This configuration routes 90% of traffic to the stable Service and 10% to the canary Service, regardless of the number of replicas.

You can also use header-based routing to send specific traffic to the canary:

apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: rollout-demo-route
spec:
  parentRefs:
  - name: example-gateway
  hostnames:
  - "rollout-demo.example.com"
  rules:
  # Rule 1: Route traffic with canary header to canary service
  - matches:
    - headers:
      - type: Exact
        name: env
        value: canary
    backendRefs:
    - name: rollout-demo-canary-service
      port: 80
  # Rule 2: Split remaining traffic 90/10
  - backendRefs:
    - name: rollout-demo-stable-service
      port: 80
      weight: 90
    - name: rollout-demo-canary-service
      port: 80
      weight: 10

This configuration sends all traffic with the env: canary header to the canary Service, while other traffic is split 90/10 between stable and canary.

Note:

The Gateway API requires a Gateway controller to be installed in your cluster. See the Gateway API documentation for installation instructions and supported implementations.

Automating canary rollouts

In production environments, canary rollouts are typically managed by controllers or CI/CD systems that automate traffic shifting, monitoring, and promotion or rollback. Tools such as Flux, Argo Rollouts, GitLab, and many others can handle progressive delivery, analysis, and rollback for you. This reduces manual steps and helps ensure safe, repeatable deployments. For more information, see the documentation for your chosen deployment tool or controller.

Cleaning up

Delete the resources created in this tutorial:

kubectl delete deployment rollout-demo-stable rollout-demo-canary
kubectl delete service rollout-demo-service

If you created separate Services for HTTPRoute, delete those as well:

kubectl delete service rollout-demo-stable-service rollout-demo-canary-service
kubectl delete httproute rollout-demo-route

What's next

Learn more about canary deployments in the Managing Workloads concept page.
Read about Deployments and how they manage your application lifecycle.
Read about Services and how they enable service discovery and load balancing.
Explore the Gateway API for advanced traffic management capabilities.
Consider using Horizontal Pod Autoscaling to automatically adjust replica counts based on metrics.

2 - Exposing an External IP Address to Access an Application in a Cluster

This page shows how to create a Kubernetes Service object that exposes an external IP address.

Before you begin

Install kubectl.
Use a cloud provider like Google Kubernetes Engine or Amazon Web Services to create a Kubernetes cluster. This tutorial creates an external load balancer, which requires a cloud provider.
Configure kubectl to communicate with your Kubernetes API server. For instructions, see the documentation for your cloud provider.

Objectives

Run five instances of a Hello World application.
Create a Service object that exposes an external IP address.
Use the Service object to access the running application.

Creating a service for an application running in five pods

Run a Hello World application in your cluster:

service/load-balancer-example.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app.kubernetes.io/name: load-balancer-example
  name: hello-world
spec:
  replicas: 5
  selector:
    matchLabels:
      app.kubernetes.io/name: load-balancer-example
  template:
    metadata:
      labels:
        app.kubernetes.io/name: load-balancer-example
    spec:
      containers:
      - image: gcr.io/google-samples/hello-app:2.0
        name: hello-world
        ports:
        - containerPort: 8080

kubectl apply -f https://k8s.io/examples/service/load-balancer-example.yaml

The preceding command creates a Deployment and an associated ReplicaSet. The ReplicaSet has five Pods each of which runs the Hello World application.

Display information about the Deployment:

kubectl get deployments hello-world
kubectl describe deployments hello-world

Display information about your ReplicaSet objects:

kubectl get replicasets
kubectl describe replicasets

Create a Service object that exposes the deployment:

kubectl expose deployment hello-world --type=LoadBalancer --name=my-service

Display information about the Service:
```
kubectl get services my-service
```
The output is similar to:
```
NAME         TYPE           CLUSTER-IP     EXTERNAL-IP      PORT(S)    AGE
my-service   LoadBalancer   10.3.245.137   104.198.205.71   8080/TCP   54s
```
Note:
The type=LoadBalancer service is backed by external cloud providers, which is not covered in this example. Please refer to setting type: LoadBalancer for your Service for the details.

Note:
If the external IP address is shown as <pending>, wait for a minute and enter the same command again.

Display detailed information about the Service:

kubectl describe services my-service

The output is similar to:

Name:           my-service
Namespace:      default
Labels:         app.kubernetes.io/name=load-balancer-example
Annotations:    <none>
Selector:       app.kubernetes.io/name=load-balancer-example
Type:           LoadBalancer
IP:             10.3.245.137
LoadBalancer Ingress:   104.198.205.71
Port:           <unset> 8080/TCP
NodePort:       <unset> 32377/TCP
Endpoints:      10.0.0.6:8080,10.0.1.6:8080,10.0.1.7:8080 + 2 more...
Session Affinity:   None
Events:         <none>

Make a note of the external IP address (LoadBalancer Ingress) exposed by your service. In this example, the external IP address is 104.198.205.71. Also note the value of Port and NodePort. In this example, the Port is 8080 and the NodePort is 32377.

In the preceding output, you can see that the service has several endpoints: 10.0.0.6:8080,10.0.1.6:8080,10.0.1.7:8080 + 2 more. These are internal addresses of the pods that are running the Hello World application. To verify these are pod addresses, enter this command:

kubectl get pods --output=wide

The output is similar to:

NAME                         ...  IP         NODE
hello-world-2895499144-1jaz9 ...  10.0.1.6   gke-cluster-1-default-pool-e0b8d269-1afc
hello-world-2895499144-2e5uh ...  10.0.1.8   gke-cluster-1-default-pool-e0b8d269-1afc
hello-world-2895499144-9m4h1 ...  10.0.0.6   gke-cluster-1-default-pool-e0b8d269-5v7a
hello-world-2895499144-o4z13 ...  10.0.1.7   gke-cluster-1-default-pool-e0b8d269-1afc
hello-world-2895499144-segjf ...  10.0.2.5   gke-cluster-1-default-pool-e0b8d269-cpuc

Use the external IP address (LoadBalancer Ingress) to access the Hello World application:
```
curl http://<external-ip>:<port>
```
where <external-ip> is the external IP address (LoadBalancer Ingress) of your Service, and <port> is the value of Port in your Service description. If you are using minikube, typing minikube service my-service will automatically open the Hello World application in a browser.

The response to a successful request is a hello message:
```
Hello, world!
Version: 2.0.0
Hostname: 0bd46b45f32f
```

Cleaning up

To delete the Service, enter this command:

kubectl delete services my-service

To delete the Deployment, the ReplicaSet, and the Pods that are running the Hello World application, enter this command:

kubectl delete deployment hello-world

What's next

Learn more about connecting applications with services. Try Deploy a Canary Release to learn how to safely test new application versions in production.

3 - Example: Deploying PHP Guestbook application with Redis

This tutorial shows you how to build and deploy a simple (not production ready), multi-tier web application using Kubernetes and Docker. This example consists of the following components:

A single-instance Redis to store guestbook entries
Multiple web frontend instances

Objectives

Start up a Redis leader.
Start up two Redis followers.
Start up the guestbook frontend.
Expose and view the Frontend Service.
Clean up.

Before you begin

Your Kubernetes server must be at or later than version v1.14.

To check the version, enter kubectl version.

Start up the Redis Database

The guestbook application uses Redis to store its data.

Creating the Redis Deployment

The manifest file, included below, specifies a Deployment controller that runs a single replica Redis Pod.

application/guestbook/redis-leader-deployment.yaml

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis-leader
  labels:
    app: redis
    role: leader
    tier: backend
spec:
  replicas: 1
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
        role: leader
        tier: backend
    spec:
      containers:
      - name: leader
        image: "registry.k8s.io/redis@sha256:cb111d1bd870a6a471385a4a69ad17469d326e9dd91e0e455350cacf36e1b3ee"
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
        ports:
        - containerPort: 6379

Launch a terminal window in the directory you downloaded the manifest files.

Apply the Redis Deployment from the redis-leader-deployment.yaml file:

kubectl apply -f https://k8s.io/examples/application/guestbook/redis-leader-deployment.yaml

Query the list of Pods to verify that the Redis Pod is running:

kubectl get pods

The response should be similar to this:

NAME                           READY   STATUS    RESTARTS   AGE
redis-leader-fb76b4755-xjr2n   1/1     Running   0          13s

Run the following command to view the logs from the Redis leader Pod:
```
kubectl logs -f deployment/redis-leader
```

Creating the Redis leader Service

The guestbook application needs to communicate to the Redis to write its data. You need to apply a Service to proxy the traffic to the Redis Pod. A Service defines a policy to access the Pods.

application/guestbook/redis-leader-service.yaml

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: v1
kind: Service
metadata:
  name: redis-leader
  labels:
    app: redis
    role: leader
    tier: backend
spec:
  ports:
  - port: 6379
    targetPort: 6379
  selector:
    app: redis
    role: leader
    tier: backend

Apply the Redis Service from the following redis-leader-service.yaml file:

kubectl apply -f https://k8s.io/examples/application/guestbook/redis-leader-service.yaml

Query the list of Services to verify that the Redis Service is running:

kubectl get service

The response should be similar to this:

NAME           TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)    AGE
kubernetes     ClusterIP   10.0.0.1     <none>        443/TCP    1m
redis-leader   ClusterIP   10.103.78.24 <none>        6379/TCP   16s

Note:

This manifest file creates a Service named redis-leader with a set of labels that match the labels previously defined, so the Service routes network traffic to the Redis Pod.

Set up Redis followers

Although the Redis leader is a single Pod, you can make it highly available and meet traffic demands by adding a few Redis followers, or replicas.

application/guestbook/redis-follower-deployment.yaml

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis-follower
  labels:
    app: redis
    role: follower
    tier: backend
spec:
  replicas: 2
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
        role: follower
        tier: backend
    spec:
      containers:
      - name: follower
        image: us-docker.pkg.dev/google-samples/containers/gke/gb-redis-follower:v2
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
        ports:
        - containerPort: 6379

Apply the Redis Deployment from the following redis-follower-deployment.yaml file:

kubectl apply -f https://k8s.io/examples/application/guestbook/redis-follower-deployment.yaml

Verify that the two Redis follower replicas are running by querying the list of Pods:

kubectl get pods

The response should be similar to this:

NAME                             READY   STATUS    RESTARTS   AGE
redis-follower-dddfbdcc9-82sfr   1/1     Running   0          37s
redis-follower-dddfbdcc9-qrt5k   1/1     Running   0          38s
redis-leader-fb76b4755-xjr2n     1/1     Running   0          11m

Creating the Redis follower service

The guestbook application needs to communicate with the Redis followers to read data. To make the Redis followers discoverable, you must set up another Service.

application/guestbook/redis-follower-service.yaml

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: v1
kind: Service
metadata:
  name: redis-follower
  labels:
    app: redis
    role: follower
    tier: backend
spec:
  ports:
    # the port that this service should serve on
  - port: 6379
  selector:
    app: redis
    role: follower
    tier: backend

Apply the Redis Service from the following redis-follower-service.yaml file:

kubectl apply -f https://k8s.io/examples/application/guestbook/redis-follower-service.yaml

Query the list of Services to verify that the Redis Service is running:

kubectl get service

The response should be similar to this:

NAME             TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
kubernetes       ClusterIP   10.96.0.1       <none>        443/TCP    3d19h
redis-follower   ClusterIP   10.110.162.42   <none>        6379/TCP   9s
redis-leader     ClusterIP   10.103.78.24    <none>        6379/TCP   6m10s

Note:

This manifest file creates a Service named redis-follower with a set of labels that match the labels previously defined, so the Service routes network traffic to the Redis Pod.

Set up and Expose the Guestbook Frontend

Now that you have the Redis storage of your guestbook up and running, start the guestbook web servers. Like the Redis followers, the frontend is deployed using a Kubernetes Deployment.

The guestbook app uses a PHP frontend. It is configured to communicate with either the Redis follower or leader Services, depending on whether the request is a read or a write. The frontend exposes a JSON interface, and serves a jQuery-Ajax-based UX.

Creating the Guestbook Frontend Deployment

application/guestbook/frontend-deployment.yaml

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: apps/v1
kind: Deployment
metadata:
  name: frontend
spec:
  replicas: 3
  selector:
    matchLabels:
        app: guestbook
        tier: frontend
  template:
    metadata:
      labels:
        app: guestbook
        tier: frontend
    spec:
      containers:
      - name: php-redis
        image: us-docker.pkg.dev/google-samples/containers/gke/gb-frontend:v5
        env:
        - name: GET_HOSTS_FROM
          value: "dns"
        resources:
          requests:
            cpu: 100m
            memory: 100Mi
        ports:
        - containerPort: 80

Apply the frontend Deployment from the frontend-deployment.yaml file:

kubectl apply -f https://k8s.io/examples/application/guestbook/frontend-deployment.yaml

Query the list of Pods to verify that the three frontend replicas are running:

kubectl get pods -l app=guestbook -l tier=frontend

The response should be similar to this:

NAME                        READY   STATUS    RESTARTS   AGE
frontend-85595f5bf9-5tqhb   1/1     Running   0          47s
frontend-85595f5bf9-qbzwm   1/1     Running   0          47s
frontend-85595f5bf9-zchwc   1/1     Running   0          47s

Creating the Frontend Service

The Redis Services you applied is only accessible within the Kubernetes cluster because the default type for a Service is ClusterIP. ClusterIP provides a single IP address for the set of Pods the Service is pointing to. This IP address is accessible only within the cluster.

If you want guests to be able to access your guestbook, you must configure the frontend Service to be externally visible, so a client can request the Service from outside the Kubernetes cluster. However a Kubernetes user can use kubectl port-forward to access the service even though it uses a ClusterIP.

Note:

Some cloud providers, like Google Compute Engine or Google Kubernetes Engine, support external load balancers. If your cloud provider supports load balancers and you want to use it, uncomment type: LoadBalancer.

application/guestbook/frontend-service.yaml

# SOURCE: https://cloud.google.com/kubernetes-engine/docs/tutorials/guestbook
apiVersion: v1
kind: Service
metadata:
  name: frontend
  labels:
    app: guestbook
    tier: frontend
spec:
  # if your cluster supports it, uncomment the following to automatically create
  # an external load-balanced IP for the frontend service.
  # type: LoadBalancer
  #type: LoadBalancer
  ports:
    # the port that this service should serve on
  - port: 80
  selector:
    app: guestbook
    tier: frontend

Apply the frontend Service from the frontend-service.yaml file:

kubectl apply -f https://k8s.io/examples/application/guestbook/frontend-service.yaml

Query the list of Services to verify that the frontend Service is running:

kubectl get services

The response should be similar to this:

NAME             TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
frontend         ClusterIP   10.97.28.230    <none>        80/TCP     19s
kubernetes       ClusterIP   10.96.0.1       <none>        443/TCP    3d19h
redis-follower   ClusterIP   10.110.162.42   <none>        6379/TCP   5m48s
redis-leader     ClusterIP   10.103.78.24    <none>        6379/TCP   11m

Viewing the Frontend Service via `kubectl port-forward`

Run the following command to forward port 8080 on your local machine to port 80 on the service.
```
kubectl port-forward svc/frontend 8080:80
```
The response should be similar to this:
```
Forwarding from 127.0.0.1:8080 -> 80
Forwarding from [::1]:8080 -> 80
```
load the page http://localhost:8080 in your browser to view your guestbook.

Viewing the Frontend Service via `LoadBalancer`

If you deployed the frontend-service.yaml manifest with type: LoadBalancer you need to find the IP address to view your Guestbook.

Run the following command to get the IP address for the frontend Service.

kubectl get service frontend

The response should be similar to this:

NAME       TYPE           CLUSTER-IP      EXTERNAL-IP        PORT(S)        AGE
frontend   LoadBalancer   10.51.242.136   109.197.92.229     80:32372/TCP   1m

Copy the external IP address, and load the page in your browser to view your guestbook.

Note:

Try adding some guestbook entries by typing in a message, and clicking Submit. The message you typed appears in the frontend. This message indicates that data is successfully added to Redis through the Services you created earlier.

Scale the Web Frontend

You can scale up or down as needed because your servers are defined as a Service that uses a Deployment controller.

Run the following command to scale up the number of frontend Pods:
```
kubectl scale deployment frontend --replicas=5
```

Query the list of Pods to verify the number of frontend Pods running:

kubectl get pods

The response should look similar to this:

NAME                             READY   STATUS    RESTARTS   AGE
frontend-85595f5bf9-5df5m        1/1     Running   0          83s
frontend-85595f5bf9-7zmg5        1/1     Running   0          83s
frontend-85595f5bf9-cpskg        1/1     Running   0          15m
frontend-85595f5bf9-l2l54        1/1     Running   0          14m
frontend-85595f5bf9-l9c8z        1/1     Running   0          14m
redis-follower-dddfbdcc9-82sfr   1/1     Running   0          97m
redis-follower-dddfbdcc9-qrt5k   1/1     Running   0          97m
redis-leader-fb76b4755-xjr2n     1/1     Running   0          108m

Run the following command to scale down the number of frontend Pods:
```
kubectl scale deployment frontend --replicas=2
```

Query the list of Pods to verify the number of frontend Pods running:

kubectl get pods

The response should look similar to this:

NAME                             READY   STATUS    RESTARTS   AGE
frontend-85595f5bf9-cpskg        1/1     Running   0          16m
frontend-85595f5bf9-l9c8z        1/1     Running   0          15m
redis-follower-dddfbdcc9-82sfr   1/1     Running   0          98m
redis-follower-dddfbdcc9-qrt5k   1/1     Running   0          98m
redis-leader-fb76b4755-xjr2n     1/1     Running   0          109m

Cleaning up

Deleting the Deployments and Services also deletes any running Pods. Use labels to delete multiple resources with one command.

Run the following commands to delete all Pods, Deployments, and Services.

kubectl delete deployment -l app=redis
kubectl delete service -l app=redis
kubectl delete deployment frontend
kubectl delete service frontend

The response should look similar to this:

deployment.apps "redis-follower" deleted
deployment.apps "redis-leader" deleted
deployment.apps "frontend" deleted
service "frontend" deleted

Query the list of Pods to verify that no Pods are running:
```
kubectl get pods
```
The response should look similar to this:
```
No resources found in default namespace.
```

What's next

Complete the Kubernetes Basics Interactive Tutorials
Use Kubernetes to create a blog using Persistent Volumes for MySQL and Wordpress
Read more about connecting applications with services
Read more about using labels effectively
Try Deploy a Canary Release to learn how to safely test new application versions in production.

Stateless Applications

1 - Deploy a Release Using a Canary Deployment

Objectives

Before you begin

Understanding canary deployments

Deploying the stable version

Creating a service

Deploying the canary version

Testing traffic distribution

Monitoring the canary deployment

Note:

Adjusting traffic distribution

Completing the rollout

Rolling back a canary deployment

Splitting traffic using HTTPRoute (optional)

Note:

Automating canary rollouts

Cleaning up

What's next

2 - Exposing an External IP Address to Access an Application in a Cluster

Before you begin

Objectives

Creating a service for an application running in five pods

Note:

Note:

Cleaning up

What's next

3 - Example: Deploying PHP Guestbook application with Redis

Objectives

Before you begin

Start up the Redis Database

Creating the Redis Deployment

Creating the Redis leader Service

Note:

Set up Redis followers

Creating the Redis follower service

Note:

Set up and Expose the Guestbook Frontend

Creating the Guestbook Frontend Deployment

Creating the Frontend Service

Note:

Viewing the Frontend Service via kubectl port-forward

Viewing the Frontend Service via LoadBalancer

Note:

Scale the Web Frontend

Cleaning up

What's next

Viewing the Frontend Service via `kubectl port-forward`

Viewing the Frontend Service via `LoadBalancer`