How do I do progressive delivery for Jump boxes with Argo CD?

Progressive delivery is an essential strategy used in modern DevOps practices to roll out changes in a controlled and gradual manner. Using Argo CD, a popular continuous delivery tool for Kubernetes, you can implement progressive delivery for jump boxes. This allows for incremental updates while minimizing disruptions.

Steps to Achieve Progressive Delivery for Jump Boxes with Argo CD

  1. Install Argo CD: Ensure that Argo CD is correctly installed and configured in your Kubernetes cluster.
  2. Create Application Manifests: Define your jump box application in the form of Kubernetes manifests.
  3. Setup a Progressive Delivery Strategy: Define a progressive delivery strategy. You might consider using feature flags to control the new feature rollouts.
  4. Monitor and Rollback: Continuously monitor the performance of the updated jump boxes and have rollback mechanisms in place.

By implementing these steps, you can ensure a smooth transition to the new jump box updates while maintaining service stability.

{ "apiVersion": "argoproj.io/v1alpha1", "kind": "Application", "metadata": { "name": "jump-box", "namespace": "argocd" }, "spec": { "destination": { "name": "in-cluster", "namespace": "jump-box-namespace" }, "source": { "repoURL": "https://github.com/your-repo/jump-box", "path": "k8s/manifests", "targetRevision": "HEAD" }, "syncPolicy": { "automated": { "prune": true, "selfHeal": true }, "syncOptions": [ "CreateNamespace=true" ] } } }

progressive delivery jump boxes Argo CD DevOps Kubernetes continuous delivery feature flags deployment strategy

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?