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OpenShift Rollouts is included in OpenShift GitOps 1.9 as a Technical Preview (TP) release. Argo Rollouts is a drop-in alternative to Deployments that supports advanced progressive delivery strategies such as blue-green and canary. While Deployments support a rollout strategy, this strategy provides limited control and tuning over the progression of the new pods. It does not support the advanced features that organizations have come to expect in cloud-native applications.

While OpenShift provides some additional capabilities over pure Deployments in this area, notably using Routes to split traffic between different services, this capability typically requires manual intervention and management. In contrast, Argo Rollouts can integrate with external metrics to easily automate the progressive delivery rollout, or rollback, of the new version of the Application.

Advanced Deployment Strategies

Organizations are moving to more frequent deployment cycles supported by adopting cloud-native technologies and more agile methodologies. As the frequencies of these deployments increase, there is a strong desire for increased safety through the use of advanced deployment strategies.

Specifically, Argo Rollouts supports two deployment strategies: blue-green and canary.

Blue-Green. In a blue-green deployment strategy, an organization would deploy a new version of the application alongside the old one as two separate deployments. Live traffic continues against the old version of the deployment, while integration and smoke testing happen against the new deployment.

Blue-green deployments

Once the organization is satisfied that the new version is fit for purpose, it can switch traffic away from the old version to the new version. If a problem is detected with live traffic in the new version, a rollback can be quickly initiated by switching the traffic back to the old version.

To summarize, blue-green deployments provide various benefits. These include quick and easy rollbacks to the previous version simply by changing the traffic routing. Integration and smoke testing can be performed in the production environment. This is useful in organizations where it is impossible to test all system components in a testing environment.

The downside of blue-green is that it requires more capacity. You must have enough resources to support two complete stacks deployed in parallel.

Canary. The canary strategy is a variation of blue-green named after the proverbial canary in the coal mine. In this deployment strategy, we again deploy a new version of the application alongside the old one. However, instead of cutting all traffic over at once, we slowly let traffic migrate to the new version over specified intervals or steps.

As the amount of live traffic increases on the new version, we can monitor the new version to check that no unexpected problems occur, such as performance issues or regressions in functionality.

Canary deployments

When using modern infrastructure like Kubernetes, you can deploy the canary with fewer replicas and then slowly increase the replicas of the new version while decreasing replicas in the old version as traffic increases. This approach eliminates the need for two equivalent stacks like blue-green and reduces the overall resource consumption.

Canary deployments provide similar benefits to blue-green while reducing the amount of infrastructure support parallel deployments.

Why Argo Rollouts?

As you can no doubt see, managing and coordinating these advanced deployment strategies in traditional infrastructure is involved. It's not unusual for teams to have long maintenance windows when performing these strategies. While automation and/or advanced infrastructure like Kubernetes can help reduce these windows, setting up these strategies still requires a substantial effort.

Argo Rollouts simplifies this process by encapsulating everything needed to define and manage the process. Argo Rollouts is a drop-in replacement for Deployment, with enhancements to enable application teams to define their rollout strategy declaratively. Teams no longer need to define multiple deployments and services for blue and green or create automation for traffic shaping and integration of tests. Rollouts will handle all of this automatically and, best of all, declaratively.

Under the hood, like Deployments, Rollouts will manage multiple ReplicaSets to represent the different versions of the deployed application. Traffic to these versions, depending on the strategy, is managed by Rollouts either through traffic shaping with supported ingress solutions (canary) or through services and selectors (blue-green).

Additional information on Rollouts and how it works is available in the community documentation here.

Installing Argo Rollouts in OpenShift

To install Rollouts, you must first install OpenShift GitOps in your cluster. Documentation for installing or upgrading to OpenShift GitOps 1.9 is available here.

Once OpenShift GitOps 1.9 is installed, navigate to the installed operators page and select the OpenShift GitOps operator. Currently, Rollouts only supports namespace mode. Select the specific OpenShift Project where you want to install Rollouts.

Installed operators

Note that in namespace mode, Rollouts only supports Rollout CustomerResources in the namespace it is running, but you can install Rollouts in multiple namespaces as needed. Namespace mode is particularly useful in multi-tenant clusters as it enables isolation between Rollout controllers.

Once you select the GitOps operator, examine the Provided APIs section. Click on Create Instance in the RolloutManager tile. This will take you to an editor where you can provide a RolloutManager CustomResource (CR) as per the example below:

apiVersion: argoproj.io/v1alpha1
kind: RolloutManager
metadata:
name: argo-rollout
labels:
example: basic
spec: {}

Click Create at the bottom of the editor to save your resource. At this point, the installation will proceed, and you can wait for the Status field of the RolloutManagers instance to change to phase: Available.

Argo Rollouts Tech Preview Limitations

Argo Rollouts is available as part of OpenShift GitOps 1.9 as a Tech Preview (TP) release. It is not officially supported for production use, but it allows customers to try it out and give feedback to improve the product before General Availability (GA). As this is TP, there are several limitations in the current iteration to be aware of.

Namespace Scope Only

This initial version of the controller in OpenShift GitOps only supports deploying Rollouts in namespace scope. This means that every namespace using Rollouts will require its own separate and dedicated instance of Rollouts in that namespace.

Future versions of the operator will include support for cluster scope.

Traffic Management

Argo Rollouts integrates with various ingress solutions to support shifting traffic between new and old releases as needed by the deployment strategy. While full support is available for OpenShift Service Mesh, at this time, OpenShift Routes are unsupported. We adding this capability. In the meantime, this means that canary deployments are on a best-effort basis determined solely by replica counts.

The following table shows the available support for deployment strategies for the ingress technologies available in OpenShift.

  OpenShift Route OpenShift Service Mesh
Blue-Green
Canary Best Effort

Metrics

The user workload metrics in OpenShift are designed for a multi-tenant environment and require authentication to access them. Unfortunately, the Prometheus provider supported by the Rollouts AnalysisTemplate does not currently support passing a bearer token. This feature is also on our roadmap. It is possible to use the Web provider as an alternative, but it is more difficult to work with. For example:

kind: AnalysisTemplate
apiVersion: argoproj.io/v1alpha1
metadata:
name: smoke-tests
spec:
args:
- name: query
- name: route-url
- name: api-token
valueFrom:
secretKeyRef:
name: monitor-auth-secret
key: token
metrics:
- name: success-rate
interval: 30s
count: 4
# NOTE: prometheus queries return results in the form of a vector.
# So it is common to access the index 0 of the returned array to obtain the value
successCondition: result[0].value[1] == '0'
failureLimit: 0
provider:
web:
url: https://thanos-querier-openshift-monitoring.apps.home.ocplab.com/api/v1/query?query={{ args.query }}
headers:
- key: Authorization
value: "Bearer {{args.api-token}}"
jsonPath: "{$.data.result}"

 

Since the Prometheus query is passed as a query parameter, it must be in an urlencoded format. Here is a sample query:

irate(haproxy_backend_http_responses_total{exported_namespace='rollouts-demo-prod', route=~"rollout-bluegreen-preview", code="5xx"}[1m]) > 5 or on() vector(0)

 

Becomes this:

irate%28haproxy_backend_http_responses_total%7Bexported_namespace%3D%27rollouts-demo-prod%27%2C%20route%3D~%22rollout-bluegreen-preview%22%2C%20code%3D%225xx%22%7D%5B1m%5D%29%20%3E%205%20or%20on%28%29%20vector%280%29

 

You can configure the Prometheus instance deployed by its operator with a username and password for OpenShift Service Mesh. This can then be used in the Prometheus provider as part of the URL, as seen below:

https://<user>:<password>@prometheus.istio-system.svc.cluster.local:9090

 

Feedback

The OpenShift GitOps product team is actively looking for feedback concerning Argo Rollouts and OpenShift integration. Customers can provide feedback via a support case or through their respective account teams.

References


About the authors

Harriet Lawrence is a product manager on the OpenShift GitOps team and is based in Scotland.

Read full bio
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