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Kubernetes Rollout Deploy step

This topic provides settings and permissions for the Kubernetes Rollout Deployment step. In Pipeline Studio, the step is labeled Rollout Deployment. On inspection, the step is labeled K8s Rollout Deploy.

Rollout Deployments​

The Rollout Deployment step performs a Kubernetes rolling update strategy. All nodes within a single environment are incrementally added one-by-one with a new service/artifact version.

The new pods are scheduled on nodes with available resources. The rolling update Deployment uses the number of pods you specified in the Service Definition Manifests (number of replicas).

Similar to application-scaling, during a rolling update of a Deployment, the Kubernetes service will load-balance the traffic only to available pods (an instance that is available to the users of the application) during the update.

What Workloads Can I Deploy?​

See What Can I Deploy in Kubernetes?.

Rolling vs Apply​

The following table lists the differences between the Rolling Deployment step (default in a Rolling strategy) and the Apply step (which may be used with any strategy).

JobsRollback
Rolling Deployment stepNoYes
Apply stepYesNo

Multiple Managed Workloads​

With the Rolling Deployment step, you can deploy multiple managed workloads.

For Canary and Blue/Green steps, only one managed object may be deployed per step by default.

You can deploy additional objects using the Apply Step, but it is typically used for deploying Jobs controllers.

You can specify the multiple workload objects in a single manifest or in individual manifests, or any other arrangement.Here is the log from a deployment where you can see both Deployment objects deployed:

apiVersion: apps/v1  
kind: Deployment  
metadata:  
  name: anshul-multiple-workloads-deployment  
spec:  
  replicas: 1  
  selector:  
    matchLabels:  
      app: anshul-multiple-workloads  
  template:  
    metadata:  
      labels:  
        app: anshul-multiple-workloads  
    spec:  
      containers:  
      - name: anshul-multiple-workloads  
        image: registry.hub.docker.com/library/nginx:stable  
        envFrom:  
        - configMapRef:  
            name: anshul-multiple-workloads  
        - secretRef:  
            name: anshul-multiple-workloads  
---  
apiVersion: apps/v1  
kind: Deployment  
metadata:  
  name: anshul-multiple-workloads-deployment-1  
spec:  
  replicas: 3  
  selector:  
    matchLabels:  
      app: anshul-multiple-workloads  
  template:  
    metadata:  
      labels:  
        app: anshul-multiple-workloads  
    spec:  
      containers:  
      - name: anshul-multiple-workloads  
        image: registry.hub.docker.com/library/nginx:stable  
        envFrom:  
        - configMapRef:  
            name: anshul-multiple-workloads  
        - secretRef:  
            name: anshul-multiple-workloads

Name​

The name for the step.

Timeout​

How long Harness should wait for this step to complete before failing it.

Skip Dry Run​

By default, Harness uses the --dry-run flag on the kubectl apply command, which prints the object that would be sent to the cluster without really sending it. If the Skip Dry Run option is selected, Harness will not use the --dry-run flag.

Advanced Settings​

See the following: