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In this article, we’ll recap how we rolled out a configuration change—namely, increasing our application replicas to five—through an automated DevOps lifecycle. You’ll learn how we streamlined development-to-production delivery, reduced manual toil, and boosted team confidence.

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Why Automate Replica Scaling?

Automating replica updates ensures consistency across environments, accelerates delivery, and minimizes human error. By embedding this change in our CI/CD pipeline, we maintained full traceability from commit to production.
Automated scaling is crucial for handling traffic spikes. Always couple replica changes with resource monitoring to validate performance.

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CI/CD Workflow for Configuration Changes

Below is the end-to-end process we followed for Sprint 07:
StageActionEnvironmentOutcome
1. CommitUpdate replica count from 3 to 5 in dev branchDevelopmentTrigger CI build
2. Build & TestCI pipeline builds Docker image and runs testsDevelopmentValidation of configuration change
3. Deploy (Dev)Deploy new image with 5 replicasDevelopmentQA sign-off
4. PromoteMerge dev → main, trigger CD to productionProductionLive application now running 5 replicas

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Pipeline Details

  1. Code Commit
    Developers update the replicas: field in the Kubernetes manifest on the development branch.
  2. Continuous Integration
  3. Development Deployment
    • Helm chart or kubectl apply deploys the image
    • Automated smoke tests validate the rollout
  4. Quality Assurance
    • QA engineers perform functional and performance tests
    • Approval triggers the merge into the main branch
  5. Production Promotion
    • CD pipeline deploys the change to production clusters
    • Monitoring alerts confirm stable operation
Before promoting to production, ensure your alerting and auto-scaling policies are configured, or you may experience resource constraints under load.

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Benefits Realized

  • Faster Feedback Loops
    Immediate testing in dev environments catches issues early.
  • Consistent Environments
    The same manifest promotes through all stages, reducing drift.
  • Reduced Manual Overhead
    Teams focus on feature work rather than repetitive deployments.

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Next Steps

  • Integrate automated performance tests in the pipeline.
  • Explore Infrastructure as Code for managing cluster configuration.
  • Implement horizontal pod auto-scalers to dynamically adjust replicas based on metrics.

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References

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