A critical topic in Cloud Native is the Microservice Architecture. We are no longer dealing with one monolithic application. We have several applications that have dependencies on each other and also have other dependencies like brokers or databases.
Applications have their own life cycle, so we should be able to execute independent canary deployment. All the applications and dependencies will not change their version at the same time.
Another important topic in the Cloud Native is Continuous Delivery. If we are going to have several applications doing canary deployment independently we have to automate it. We will use Helm, Argo Rollouts, Openshift Service Mesh, Openshift GitOps, and of course Red Hat Openshift to help us.
Argo Rollouts is a Kubernetes controller and set of CRDs which provide advanced deployment capabilities such as blue-green, canary, canary analysis, experimentation, and progressive delivery features to Kubernetes. In this demo we are going to use canary capabilities.
In the next steps, we will see a real example of how to install, deploy and manage the life cycle of Cloud Native applications doing canary deployment using Argo Rollouts and Openshift Service Mesh.
Let's start with some theory...after that, we will have a hands-on example.
A canary deployment is a strategy where the operator releases a new version of their application to a small percentage of the production traffic. This small percentage may test the new version and provide feedback. If the new version is working well the operator may increase the percentage, till all the traffic is using the new version. Unlike Blue/Green, canary deployments are smoother, and failures have limited impact.
We are going to use very simple applications to test canary deployment. We have created two Quarkus applications Products and Discounts
Products call Discounts to get the product`s discount and expose an API with a list of products with its discounts.
To achieve canary deployment with Cloud Native applications using Argo Rollouts and Openshift Service Mesh, we have designed this architecture. This is a simplification.
OpenShift Components - Online
- Route, Gateway and Virtual Services.
- Services mapped to the rollout.
In Blue/Green deployment we always have an offline service to test the version that is not in production. In the case of canary deployment we do not need it because progressively we will have the new version in production.
We have defined an active or online service 'products-umbrella-online'. The final user will always use 'products-umbrella-online'. When a new version is deployed Argo Rollouts create a new revision (ReplicaSet). The number of replicas in the new release increases based on the information in the steps, the number of replicas in the old release decreases in the same number. Argo Rollouts will also automatically change in the Virtual Service, the amount of traffic that is sent to each revision. We have configured a pause duration between each step. To learn more about Argo Rollouts, please read this.
One of the best ways to package Cloud Native applications is Helm. In canary deployment it makes even more sense.
We have created a chart for each application that does not know anything about canary. Then we pack everything together in an umbrella helm chart.
In the Shop Umbrella Chart we use several times the same charts as helm dependencies but with different names.
We have packaged both applications in one chart, but we may have different umbrella charts per application.
- Red Hat Openshift 4.16 with admin rights.
- You can download Red Hat Openshift Local for OCP 4.16.
- Getting Started Guide
- Git
- GitHub account
- oc 4.16 CLI
- Argo Rollouts CLI
We have a GitHub repository for this demo. As part of the demo, you will have to do some changes and commits. So it is important that you fork the repository and clone it in your local.
git clone https://github.com/your_user/cloud-native-deployment-strategies
If we want to have a Cloud Native deployment we can not forget CI/CD. Red Hat OpenShift GitOps will help us.
Go to the folder where you have cloned your forked repository and create a new branch rollouts-mesh
git checkout -b rollouts-mesh
git push origin rollouts-mesh
Log into OpenShift as a cluster admin and install the OpenShift GitOps operator with the following command. This may take some minutes.
oc apply -f gitops/gitops-operator.yaml
Once OpenShift GitOps is installed, an instance of Argo CD is automatically installed on the cluster in the openshift-gitops namespace and a link to this instance is added to the application launcher in OpenShift Web Console.
Argo CD upon installation generates an initial admin password which is stored in a Kubernetes secret. In order to retrieve this password, run the following command to decrypt the admin password:
oc extract secret/openshift-gitops-cluster -n openshift-gitops --to=-
Click on Argo CD from the OpenShift Web Console application launcher and then log into Argo CD with admin username and the password retrieved from the previous step.
We are going to follow, as much as we can, a GitOps methodology in this demo. So we will have everything in our Git repository and use ArgoCD to deploy it in the cluster.
In the current Git repository, the gitops/cluster-config directory contains OpenShift cluster configurations such as:
- namespaces
gitops. - role binding for ArgoCD to the namespace
gitops. - Argo Rollouts project.
- OpenShift Service Mesh
- Kiali Operator
- OpenShift Elasticsearch Operator
- Red Hat OpenShift distributed tracing platform
Let's configure Argo CD to recursively sync the content of the gitops/cluster-config directory into the OpenShift cluster.
Execute this command to add a new Argo CD application that syncs a Git repository containing cluster configurations with the OpenShift cluster.
oc apply -f canary-argo-rollouts-service-mesh/application-cluster-config.yaml
Looking at the Argo CD dashboard, you will notice that an application has been created.
You can click on the cluster-configuration application to check the details of sync resources and their status on the cluster.
We are going to create the application shop, that we will use to test canary deployment. Because we will make changes in the application's GitHub repository, we have to use the repository that you have just forked. Please edit the file canary-argo-rollouts-service-mesh/application-shop-canary-rollouts-mesh.yaml and set your own GitHub repository in the reportURLand the OCP cluster domain in change_domain.
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: shop
namespace: openshift-gitops
spec:
destination:
name: ''
namespace: gitops
server: 'https://kubernetes.default.svc'
source:
path: helm/quarkus-helm-umbrella/chart
repoURL: https://github.com/change_me/cloud-native-deployment-strategies.git
targetRevision: rollouts-mesh
helm:
parameters:
- name: "global.namespace"
value: gitops
- name: "domain"
value: "change_domain"
valueFiles:
- values/values-canary-rollouts-mesh.yaml
project: default
syncPolicy:
automated:
prune: true
selfHeal: trueoc apply -f canary-argo-rollouts-service-mesh/application-shop-canary-rollouts-mesh.yaml
Looking at the Argo CD dashboard, you will notice that we have a new shop application.
We have deployed the shop with ArgoCD. We can test that it is up and running.
We have to get the route that we have created.
oc get routes shop-umbrella-products-route -n istio-system --template='https://{{.spec.host}}/products'
Notice that in each microservice response, we have added metadata information to see better the version of each application. This will help us to see the changes while we do the canary deployment.
We can see that the current version is v1.0.1:
{
"products":[
{
...
"name":"TV 4K",
"price":"1500€"
}
],
"metadata":{
"version":"v1.0.1", <--
"colour":"none",
"mode":"online"
}
}We can also see the rollout`s status1.
kubectl argo rollouts get rollout products --watch -n gitops
NAME KIND STATUS AGE INFO
⟳ products Rollout ✔ Healthy 38s
└──# revision:1
└──⧉ products-67fc9fb79b ReplicaSet ✔ Healthy 38s stable
├──□ products-67fc9fb79b-4ql4z Pod ✔ Running 38s ready:1/1
├──□ products-67fc9fb79b-7c4jw Pod ✔ Running 38s ready:1/1
├──□ products-67fc9fb79b-lz86j Pod ✔ Running 38s ready:1/1
└──□ products-67fc9fb79b-xlkhp Pod ✔ Running 38s ready:1/1
We have already deployed the products version v1.0.1 with 4 replicas, and we are ready to use a new products version v1.1.1 that has a new description attribute.
This is our current status:
This is how we have configure Argo Rollouts for this demo:
strategy:
canary:
steps:
- setWeight: 10
- pause:
duration: 30s
- setWeight: 50
- pause:
duration: 30sWe have split a Cloud Native Canary deployment into three automatic step:
- Deploy canary version for 10%
- Scale canary version to 50%
- Scale canary version to 100%
This is just an example. The key point here is that, very easily we can have the canary deployment that better fits our needs. To make this demo faster we have not set a pause without duration in any step, so Argo Rollouts will go throw each step automatically.
We will deploy a new version v1.1.1. To do it, we have to edit the file helm/quarkus-helm-umbrella/chart/values/values-canary-rollouts-mesh.yaml under products-blue set tag value to v1.1.1
products-blue:
quarkus-base:
image:
tag: v1.1.1Argo Rollouts will automatically deploy a new products revision and change the Virtual Service to send 10% of the traffic to the canary version. Argo Rollouts also makes a best effort attempt to achieve the percentage listed in the last setWeight step between the new and old version. This means that it will create only one replica in the new revision, because it is rounded up. All the requests are load balanced between the old and the new replicas.
Push the changes to start the deployment.
git add .
git commit -m "Change products version to v1.1.1"
git push origin rollouts-mesh
ArgoCD will refresh the status after some minutes. If you don't want to wait you can refresh it manually from ArgoCD UI or configure the Argo CD Git Webhook.2.
This is our current status:
kubectl argo rollouts get rollout products --watch -n gitops
NAME KIND STATUS AGE INFO
⟳ products Rollout ॥ Paused 3m13s
├──# revision:2
│ └──⧉ products-9dc6f576f ReplicaSet ✔ Healthy 8s canary
│ └──□ products-9dc6f576f-fwq8m Pod ✔ Running 8s ready:1/1
└──# revision:1
└──⧉ products-67fc9fb79b ReplicaSet ✔ Healthy 3m13s stable
├──□ products-67fc9fb79b-4ql4z Pod ✔ Running 3m13s ready:1/1
├──□ products-67fc9fb79b-lz86j Pod ✔ Running 3m13s ready:1/1
└──□ products-67fc9fb79b-xlkhp Pod ✔ Running 3m13s ready:1/1
In the products url`s response you will have the new version in 10% of the requests.
New revision:
{
"products":[
{
"discountInfo":{...},
"name":"TV 4K",
"price":"1500€",
"description":"The best TV" <--
}
],
"metadata":{
"version":"v1.1.1", <--
}
}Old revision:
{
"products":[
{
"discountInfo":{...},
"name":"TV 4K",
"price":"1500€"
}
],
"metadata":{
"version":"v1.0.1", <--
}
}After 30 seconds Argo Rollouts automatically will increase the number of replicas in the new release to 2 and change the Virtual Service to send 50% of the traffic to the canary version. Instead of increasing automatically after 30 seconds we can configure Argo Rollouts to wait indefinitely until that Pause condition is removed. But this is not part of this demo.
This is our current status:
kubectl argo rollouts get rollout products --watch -n gitops
NAME KIND STATUS AGE INFO
⟳ products Rollout ॥ Paused 3m47s
├──# revision:2
│ └──⧉ products-9dc6f576f ReplicaSet ✔ Healthy 42s canary
│ ├──□ products-9dc6f576f-fwq8m Pod ✔ Running 42s ready:1/1
│ └──□ products-9dc6f576f-8qppq Pod ✔ Running 6s ready:1/1
└──# revision:1
└──⧉ products-67fc9fb79b ReplicaSet ✔ Healthy 3m47s stable
├──□ products-67fc9fb79b-lz86j Pod ✔ Running 3m47s ready:1/1
└──□ products-67fc9fb79b-xlkhp Pod ✔ Running 3m47s ready:1/1
After other 30 seconds Argo Rollouts will increase the number of replicas in the new release to 4 and scale down the old revision. It also changes the Virtual Service to send 100% of the traffic to the canary version.
This is our current status:
kubectl argo rollouts get rollout products --watch -n gitops
NAME KIND STATUS AGE INFO
⟳ products Rollout ✔ Healthy 4m32s
├──# revision:2
│ └──⧉ products-9dc6f576f ReplicaSet ✔ Healthy 87s stable
│ ├──□ products-9dc6f576f-fwq8m Pod ✔ Running 87s ready:1/1
│ ├──□ products-9dc6f576f-8qppq Pod ✔ Running 51s ready:1/1
│ ├──□ products-9dc6f576f-5ch92 Pod ✔ Running 17s ready:1/1
│ └──□ products-9dc6f576f-kmvdh Pod ✔ Running 17s ready:1/1
└──# revision:1
└──⧉ products-67fc9fb79b ReplicaSet • ScaledDown 4m32s
We have in the online environment the new version v1.1.1!!!
{
"products":[
{
"discountInfo":{...},
"name":"TV 4K",
"price":"1500€",
"description":"The best TV" <--
}
],
"metadata":{
"version":"v1.1.1", <--
}
}To delete all the things that we have done for the demo you have to:
- In GitHub delete the branch
rollouts-mesh - In ArgoCD delete the application
cluster-configurationandshop - In Openshift, go to project
openshift-operatorsand delete the installed operators Openshift GitOps, OpenShift Service Mesh, Kiali Operator, OpenShift Elasticsearch Operator, Red Hat OpenShift distributed tracing platform
Footnotes
-
Argo Rollouts offers a Kubectl plugin to enrich the experience with Rollouts https://argoproj.github.io/argo-rollouts/installation/#kubectl-plugin-installation ↩
