GCP DevOps Project

Sprint 07

Sprint 07 review

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.

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.

Note

Automated scaling is crucial for handling traffic spikes. Always couple replica changes with resource monitoring to validate performance.

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 devmain, trigger CD to productionProductionLive application now running 5 replicas

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

Warning

Before promoting to production, ensure your alerting and auto-scaling policies are configured, or you may experience resource constraints under load.

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.

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.

References

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