Minikube Application Deployment Patterns: Production-Ready Manifests for Four Common Workloads

Choosing the Right Workload Type#

Every application fits one of four deployment patterns. Choosing the wrong one creates problems that are hard to fix later – a database deployed as a Deployment loses data on reschedule, a batch job deployed as a Deployment wastes resources running 24/7.

Pattern Kubernetes Resource Use When
Stateless web app Deployment + Service + Ingress HTTP APIs, frontends, microservices
Stateful app StatefulSet + Headless Service + PVC Databases, caches with persistence, message brokers
Background worker Deployment (no Service) Queue consumers, event processors, stream readers
Batch processing CronJob Scheduled reports, data cleanup, periodic syncs

Pattern 1: Stateless Web App#

A web API that can be scaled horizontally with no persistent state. Any pod can handle any request.

Change Management for Infrastructure

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Why Change Management Matters#

Most production incidents trace back to a change. Code deployments, configuration updates, infrastructure modifications, database migrations – each introduces risk. Change management reduces that risk through structure, visibility, and accountability. The goal is not to prevent change but to make change safe, visible, and reversible.

Change Request Process#

Every infrastructure change flows through a structured request. The formality scales with risk, but the basic elements remain constant.

GitHub Actions Kubernetes Pipeline: From Git Push to Helm Deploy

GitHub Actions Kubernetes Pipeline#

This guide builds a complete pipeline: push code, build a container image, validate the Helm chart, and deploy to Kubernetes. Each stage gates the next, so broken images never reach your cluster.

Pipeline Overview#

The pipeline has four stages:

  1. Build and push the container image to GitHub Container Registry (GHCR).
  2. Lint and validate the Helm chart with helm lint and kubeconform.
  3. Deploy to dev automatically on pushes to main.
  4. Promote to staging and production via manual approval.

Complete Workflow File#

# .github/workflows/deploy.yml
name: Build and Deploy

on:
  push:
    branches: [main]
  workflow_dispatch:
    inputs:
      environment:
        description: "Target environment"
        required: true
        type: choice
        options: [dev, staging, production]

env:
  REGISTRY: ghcr.io
  IMAGE_NAME: ${{ github.repository }}

jobs:
  build:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      packages: write
    outputs:
      image-tag: ${{ steps.meta.outputs.version }}
    steps:
      - uses: actions/checkout@v4

      - name: Log in to GHCR
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Extract metadata
        id: meta
        uses: docker/metadata-action@v5
        with:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
          tags: |
            type=sha,prefix=
            type=ref,event=branch

      - name: Build and push
        uses: docker/build-push-action@v6
        with:
          context: .
          push: true
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}

  validate:
    runs-on: ubuntu-latest
    needs: build
    steps:
      - uses: actions/checkout@v4

      - name: Install Helm
        uses: azure/setup-helm@v4

      - name: Helm lint
        run: helm lint ./charts/my-app -f charts/my-app/values.yaml

      - name: Install kubeconform
        run: |
          curl -sL https://github.com/yannh/kubeconform/releases/latest/download/kubeconform-linux-amd64.tar.gz \
            | tar xz -C /usr/local/bin

      - name: Validate rendered templates
        run: |
          helm template my-app ./charts/my-app \
            --set image.tag=${{ needs.build.outputs.image-tag }} \
            | kubeconform -strict -summary \
              -kubernetes-version 1.29.0

  deploy-dev:
    runs-on: ubuntu-latest
    needs: [build, validate]
    if: github.ref == 'refs/heads/main'
    environment: dev
    steps:
      - uses: actions/checkout@v4

      - name: Install Helm
        uses: azure/setup-helm@v4

      - name: Set up kubeconfig
        run: |
          mkdir -p ~/.kube
          echo "${{ secrets.KUBECONFIG_DEV }}" | base64 -d > ~/.kube/config
          chmod 600 ~/.kube/config

      - name: Deploy with Helm
        run: |
          helm upgrade --install my-app ./charts/my-app \
            --namespace my-app-dev \
            --create-namespace \
            -f charts/my-app/values-dev.yaml \
            --set image.tag=${{ needs.build.outputs.image-tag }} \
            --wait --timeout 300s

      - name: Verify deployment
        run: kubectl rollout status deployment/my-app -n my-app-dev --timeout=120s

  deploy-staging:
    runs-on: ubuntu-latest
    needs: [build, validate, deploy-dev]
    environment: staging
    steps:
      - uses: actions/checkout@v4

      - name: Install Helm
        uses: azure/setup-helm@v4

      - name: Set up kubeconfig
        run: |
          mkdir -p ~/.kube
          echo "${{ secrets.KUBECONFIG_STAGING }}" | base64 -d > ~/.kube/config
          chmod 600 ~/.kube/config

      - name: Deploy with Helm
        run: |
          helm upgrade --install my-app ./charts/my-app \
            --namespace my-app-staging \
            --create-namespace \
            -f charts/my-app/values-staging.yaml \
            --set image.tag=${{ needs.build.outputs.image-tag }} \
            --wait --timeout 300s

  deploy-production:
    runs-on: ubuntu-latest
    needs: [build, validate, deploy-staging]
    environment: production
    steps:
      - uses: actions/checkout@v4

      - name: Install Helm
        uses: azure/setup-helm@v4

      - name: Set up kubeconfig
        run: |
          mkdir -p ~/.kube
          echo "${{ secrets.KUBECONFIG_PROD }}" | base64 -d > ~/.kube/config
          chmod 600 ~/.kube/config

      - name: Deploy with Helm
        run: |
          helm upgrade --install my-app ./charts/my-app \
            --namespace my-app-prod \
            --create-namespace \
            -f charts/my-app/values-production.yaml \
            --set image.tag=${{ needs.build.outputs.image-tag }} \
            --wait --timeout 300s

Key Design Decisions#

Image Tagging with Git SHA#

The docker/metadata-action generates tags from the git SHA. This creates immutable, traceable image tags – you can always identify exactly which commit produced a given deployment.

Scenario: Recovering from a Failed Deployment

Scenario: Recovering from a Failed Deployment#

You are helping when someone reports: “we deployed a new version and it is causing errors,” “pods are not starting,” or “the service is down after a deploy.” The goal is to restore service as quickly as possible, then prevent recurrence.

Time matters here. Every minute of diagnosis while the service is degraded is a minute of user impact. The bias should be toward fast rollback first, then root cause analysis second.

Choosing a GitOps Tool: ArgoCD vs Flux vs Jenkins vs GitHub Actions for Kubernetes Deployments

Choosing a GitOps Tool#

The term “GitOps” is applied to everything from a simple kubectl apply in a GitHub Actions workflow to a fully reconciled, pull-based deployment architecture with drift detection. These are fundamentally different approaches. Choosing between them depends on your team’s operational maturity, cluster count, and tolerance for running controllers in your cluster.

What GitOps Actually Means#

GitOps, as defined by the OpenGitOps principles (a CNCF sandbox project), has four requirements: declarative desired state, state versioned in git, changes applied automatically, and continuous reconciliation with drift detection. The last two are what separate true GitOps from “CI/CD that uses git.”