How do I do progressive delivery for Infrastructure as Code with Argo CD?

Progressive delivery for Infrastructure as Code (IaC) using Argo CD is a crucial strategy for implementing robust change management in your deployments. By gradually introducing changes to your infrastructure, you can ensure higher reliability and efficiency while minimizing risks. In this tutorial, we will explore how to effectively set up progressive delivery with Argo CD.

Argo CD is a declarative, GitOps continuous delivery tool for Kubernetes. Using its powerful features, we can manage and roll out changes to our infrastructure in a phased manner. Below is a step-by-step example of how to achieve this.

// Step 1: Create an Argo CD Application apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: my-app namespace: argocd spec: project: default source: repoURL: 'git@github.com:my-org/my-repo.git' targetRevision: HEAD path: my-app destination: server: 'https://kubernetes.default.svc' namespace: my-namespace syncPolicy: automated: prune: true selfHeal: true syncOptions: - CreateNamespace=true // Step 2: Define progressive delivery strategies like canary or blue-green syncWindow: manualSync: true

Once you define your application in Argo CD, you can initiate progressive delivery by gradually adjusting the weight of traffic routed to new versions through canary releases, or by using feature flags.

As you apply updates, monitor the health and performance of your infrastructure. If issues arise, Argo CD allows you to roll back changes with ease, ensuring your systems remain stable.


Progressive Delivery Infrastructure as Code Argo CD Continuous Delivery GitOps Kubernetes Canary Releases Blue-Green Deployments

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?