Cloud Service Mesh by example: Canary Deployments

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In this tutorial, you will walk through a common use case of rolling out a canary deployment with Cloud Service Mesh.

What is a canary deployment?

A canary deployment routes a small percentage of traffic to a new version of a microservice, then allows you to gradually roll out to the whole user base, while phasing out and retiring the old version. If something goes wrong during this process, traffic can be switched back to the old version. With Cloud Service Mesh, you can route traffic to ensure that new services are introduced safely.

Costs

In this document, you use the following billable components of Google Cloud:

• GKE
• Cloud Service Mesh

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish this tutorial, you can avoid ongoing costs by deleting the resources you created. For more information, see Clean up.

Before you begin

• Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.

• Install managed Cloud Service Mesh* on a GKE cluster.

  Note: This tutorial also works for managed Cloud Service Mesh via asmcli and in-cluster Cloud Service Mesh.

• Deploy an Ingress Gateway.

• Clone the repo:

  git clone https://github.com/GoogleCloudPlatform/anthos-service-mesh-samples
  cd anthos-service-mesh-samples/docs/canary-service
  

Deploy Online Boutique

1. Set the current context for kubectl to the cluster where you deployed Online Boutique:

   gcloud container clusters get-credentials CLUSTER_NAME  \
   --project=PROJECT_ID \
   --zone=CLUSTER_LOCATION 
   

2. Create the namespace for the sample application and the ingress gateway:

   kubectl create namespace onlineboutique
   

3. Label the onlineboutique namespace to automatically inject Envoy proxies. Follow the steps on how to enable automatic sidecar injection.

4. Deploy the sample app. For this tutorial, you will deploy Online Boutique, a microservice demo app.

   kubectl apply \
   -n onlineboutique \
   -f https://raw.githubusercontent.com/GoogleCloudPlatform/anthos-service-mesh-samples/main/docs/shared/online-boutique/kubernetes-manifests.yaml
   

5. Add a label version=v1 to the productcatalog deployment by running the following command:

   kubectl patch deployments/productcatalogservice -p '{"spec":{"template":{"metadata":{"labels":{"version":"v1"}}}}}' \
   -n onlineboutique
   

   View your services you have deployed:

   kubectl get pods -n onlineboutique
   

   Expected output:

   NAME                                     READY   STATUS    RESTARTS   AGE
   adservice-85598d856b-m84m6               2/2     Running   0          2m7s
   cartservice-c77f6b866-m67vd              2/2     Running   0          2m8s
   checkoutservice-654c47f4b6-hqtqr         2/2     Running   0          2m10s
   currencyservice-59bc889674-jhk8z         2/2     Running   0          2m8s
   emailservice-5b9fff7cb8-8nqwz            2/2     Running   0          2m10s
   frontend-77b88cc7cb-mr4rp                2/2     Running   0          2m9s
   loadgenerator-6958f5bc8b-55q7w           2/2     Running   0          2m8s
   paymentservice-68dd9755bb-2jmb7          2/2     Running   0          2m9s
   productcatalogservice-84f95c95ff-c5kl6   2/2     Running   0          114s
   recommendationservice-64dc9dfbc8-xfs2t   2/2     Running   0          2m9s
   redis-cart-5b569cd47-cc2qd               2/2     Running   0          2m7s
   shippingservice-5488d5b6cb-lfhtt         2/2     Running   0          2m7s
   

   All of the pods for your application should be up and running, with a 2/2 in the READY column. This indicates that the pods have an Envoy sidecar proxy injected successfully.

6. Deploy your VirtualService and DestinationRule for v1 of productcatalog:

   kubectl apply -f destination-vs-v1.yaml -n onlineboutique
   

   apiVersion: networking.istio.io/v1beta1
   kind: DestinationRule
   metadata:
     name: productcatalogservice
   spec:
     host: productcatalogservice
     subsets:
     - labels:
         version: v1
       name: v1
   ---
   apiVersion: networking.istio.io/v1beta1
   kind: VirtualService
   metadata:
     name: productcatalogservice
   spec:
     hosts:
     - productcatalogservice
     http:
     - route:
       - destination:
           host: productcatalogservice
           subset: v1

   Note that only v1 is present in the resources.

7. Visit the application in your browser using the external IP address of your ingress:

   kubectl get services -n GATEWAY_NAMESPACE
   

This next section will tour the Cloud Service Mesh UI and show how you can view your metrics.

Deploy and View your services in Google Cloud console

1. In Google Cloud console, go the GKE Enterprise Services page.

   Go to GKE Enterprise Services

2. By default, you view your services in the Table view.

   The Table Overview lets you observe all your services, as well as important metrics at a glance.

   

3. In the top right, click on Topology. Here you can view your services and their interaction with each other.

   You can expand services and view the requests per second for each of your services by hovering over on them with your cursor.

   

4. Navigate back to the Table View.

5. In the Services Table, select productcatalogservice. This will take you to an overview of your service.

6. On the left side of the screen, click Traffic.

7. Ensure 100% of the incoming traffic to productcatalogservice goes to the workload service.

   

The next section will go through creating a v2 of the productcatalog service.

Deploy v2 of a service

1. For this tutorial, productcatalogservice-v2 will introduce a 3-second latency into requests with the EXTRA_LATENCY field.

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: productcatalogservice-v2
   spec:
     selector:
       matchLabels:
         app: productcatalogservice
     template:
       metadata:
         labels:
           app: productcatalogservice
           version: v2
       spec:
         containers:
         - env:
           - name: PORT
             value: '3550'
           - name: EXTRA_LATENCY
             value: 3s
           name: server
           image: gcr.io/google-samples/microservices-demo/productcatalogservice:v0.3.6
           livenessProbe:
             exec:
               command: ["/bin/grpc_health_probe", "-addr=:3550"]
           ports:
           - containerPort: 3550
           readinessProbe:
             exec:
               command: ["/bin/grpc_health_probe", "-addr=:3550"]
           resources:
             limits:
               cpu: 200m
               memory: 128Mi
             requests:
               cpu: 100m
               memory: 64Mi
         terminationGracePeriodSeconds: 5

   Apply this resource to the onlineboutiquenamespace.

   kubectl apply -f productcatalog-v2.yaml -n onlineboutique
   

2. Check on your application pods.

   kubectl get pods -n onlineboutique
   

   Expected output:

   NAME                                     READY   STATUS    RESTARTS   AGE
   adservice-85598d856b-8wqfd                  2/2     Running   0          25h
   cartservice-c77f6b866-7jwcr                 2/2     Running   0          25h
   checkoutservice-654c47f4b6-n8c6x            2/2     Running   0          25h
   currencyservice-59bc889674-l5xw2            2/2     Running   0          25h
   emailservice-5b9fff7cb8-jjr89               2/2     Running   0          25h
   frontend-77b88cc7cb-bwtk4                   2/2     Running   0          25h
   loadgenerator-6958f5bc8b-lqmnw              2/2     Running   0          25h
   paymentservice-68dd9755bb-dckrj             2/2     Running   0          25h
   productcatalogservice-84f95c95ff-ddhjv      2/2     Running   0          25h
   productcatalogservice-v2-6df4cf5475-9lwjb   2/2     Running   0          8s
   recommendationservice-64dc9dfbc8-7s7cx      2/2     Running   0          25h
   redis-cart-5b569cd47-vw7lw                  2/2     Running   0          25h
   shippingservice-5488d5b6cb-dj5gd            2/2     Running   0          25h
   

   Note that there are now two productcatalogservices listed.

3. The DestinationRule is how to specify the subsets of a service. In this scenario, there is a subset for v1 and for v2 of productcatalogservice.

   apiVersion: networking.istio.io/v1beta1
   kind: DestinationRule
   metadata:
     name: productcatalogservice
   spec:
     host: productcatalogservice
     subsets:
     - labels:
         version: v1
       name: v1
     - labels:
         version: v2
       name: v2

   Note the labels field. The versions of productcatalogservice are distinguished after the traffic is routed by the VirtualService.

   Apply the DestinationRule:

   kubectl apply -f destination-v1-v2.yaml -n onlineboutique
   

Split Traffic between v1 and v2

1. A VirtualService is how you introduce a small percentage of the traffic to direct to v2 of the productcatalogservice.

   apiVersion: networking.istio.io/v1beta1
   kind: VirtualService
   metadata:
     name: productcatalogservice
   spec:
     hosts:
     - productcatalogservice
     http:
     - route:
       - destination:
           host: productcatalogservice
           subset: v1
         weight: 75
       - destination:
           host: productcatalogservice
           subset: v2
         weight: 25

   The subset field indicates the version, and the weight field indicates the percentage split of traffic. 75% of traffic will go to v1 of productcatalog, and 25% will go to v2.

   Apply the VirtualService:

   kubectl apply -f vs-split-traffic.yaml -n onlineboutique
   

If you visit the EXTERNAL_IP of the cluster's ingress, you should notice that periodically, the frontend is slower to load.

In the next section, you will explore the traffic split in GKE Enterprise Google Cloud console.

Observe the Traffic Split in Google Cloud console

1. Return to Google Cloud console and go to GKE Enterprise Services page Go to GKE Enterprise Services

2. In the top right, click on Topology.

   Expand the productcatalogservice workload. You will see productcatalogservice and productcatalogservice-v2 deployments.

   

3. Return to the Table View. Click productcatalogservice in the Services Table. Return to Traffic on the left navigation bar.

4. Note that the incoming traffic is split between v1 and v2 by the percentage specified in the VirtualService file, and that there are 2 workloads of the productcatalog service.

   On the right side of the screen, you will see Requests, Error Rate, and Latency Metrics. With Cloud Service Mesh, each service will have these metrics outlined to provide you the observability.

   

   Note: It will take some time for the actual 75-25 split to be shown, since traffic over time will be directed to the v2 service.

Rollout or Rollback to a version

After observing the metrics during a canary deployment, you can rollout to the new service, or rollback to the old service by leveraging the VirtualService resource.

Rollout

Once you are satisfied with the behavior of a v2 service, incrementally increase the behavior of traffic to the v2 service. Eventually, traffic can be directed 100% to the new service.

apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: productcatalogservice
spec:
  hosts:
  - productcatalogservice
  http:
  - route:
    - destination:
        host: productcatalogservice
        subset: v2

To direct all the traffic to v2 of productcatalogservice:

kubectl apply -f vs-v2.yaml -n onlineboutique

Rollback

If you need to roll back to the v1 service, simply apply the destination-vs-v1.yaml from earlier. This will direct traffic only to v1 of productcatalogservice.

apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: productcatalogservice
spec:
  hosts:
  - productcatalogservice
  http:
  - route:
    - destination:
        host: productcatalogservice
        subset: v1

To direct all the traffic to v1 of productcatalogservice:

kubectl apply -f vs-v1.yaml -n onlineboutique

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

To avoid incurring continuing charges to your Google Cloud account for the resources used in this tutorial, you can either delete the project or delete the individual resources.

Delete the project

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

1. In Cloud Shell, delete the project:

   gcloud projects delete PROJECT_ID
   

Delete the resources

• If you want to prevent additional charges, delete the cluster:

  gcloud container clusters delete  CLUSTER_NAME  \
  --project=PROJECT_ID \
  --zone=CLUSTER_LOCATION 
  

• If you want to keep your cluster and remove the Online Boutique sample:

  1. Delete the application namespaces:

     kubectl delete -f namespace onlineboutique
     

     Expected output:

     namespace "onlineboutique" deleted
     

  2. Delete the service entries:

     kubectl delete -f https://raw.githubusercontent.com/GoogleCloudPlatform/microservices-demo/main/istio-manifests/frontend.yaml -n onlineboutique
     kubectl delete -f https://raw.githubusercontent.com/GoogleCloudPlatform/microservices-demo/main/istio-manifests/frontend-gateway.yaml -n onlineboutique
     

     Expected output:

     serviceentry.networking.istio.io "allow-egress-googleapis" deleted
     serviceentry.networking.istio.io "allow-egress-google-metadata" deleted
     

What's next

• For a general guide on configuring PeerAuthentication policies, see Configuring transport security.