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:
| Stage | Action | Environment | Outcome |
|---|---|---|---|
| 1. Commit | Update replica count from 3 to 5 in dev branch | Development | Trigger CI build |
| 2. Build & Test | CI pipeline builds Docker image and runs tests | Development | Validation of configuration change |
| 3. Deploy (Dev) | Deploy new image with 5 replicas | Development | QA sign-off |
| 4. Promote | Merge dev → main, trigger CD to production | Production | Live application now running 5 replicas |
Pipeline Details
Code Commit
Developers update thereplicas:field in the Kubernetes manifest on thedevelopmentbranch.Continuous Integration
- Build Docker image
- Run unit tests and linters
- Push image to container registry
- Learn more about CI/CD
Development Deployment
- Helm chart or
kubectl applydeploys the image - Automated smoke tests validate the rollout
- Helm chart or
Quality Assurance
- QA engineers perform functional and performance tests
- Approval triggers the merge into the
mainbranch
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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