Introduction
Building a production-ready microservices architecture involves more than just writing code. It requires a robust delivery pipeline, automated infrastructure, and deep observability. In my latest project, ShopMicro-Production, I set out to build a fully automated e-commerce engine deployed on Amazon EKS.
The Stack
The platform follows a classic microservices pattern:
- Frontend: A sleek React/Vite interface served via Nginx.
- Backend: A Node.js/Express API handling the core logic.
- ML Service: A Python/Flask recommendation engine for intelligent product suggestions.
- Data Layers: PostgreSQL for persistent storage and Redis for high-performance caching.
Infrastructure as Code (IaC)
One of the core principles of this project was "Everything as Code." I used Terraform to provision the entire AWS EKS cluster, including managed node groups and all necessary IAM roles.
Early on, I also experimented with Ansible to bootstrap self-managed Kubernetes nodes on EC2, which provided a deep understanding of control plane orchestration before moving to the managed EKS experience.
The CI/CD Engine
The automation is powered by GitHub Actions with four distinct pipelines:
- App CI: Automatically runs linting and unit tests on every PR, then builds and pushes Docker images to GHCR.
- App CD: Sequentially deploys services to EKS, ensuring dependencies like Redis and Postgres are ready before the apps start.
-
IaC CI: Validates Terraform code using
tflintand ensures compliance with OPA (Open Policy Agent) policies. - Drift Detection: A daily automated check to ensure no manual changes have deviated from our Terraform source of truth.
Zero-Downtime Reliability
To ensure the system stays healthy:
- HPA (Horizontal Pod Autoscaler): Automatically scales the backend and ML services based on CPU/Memory thresholds.
-
Rollback Proof: Implemented a "fail-safe" procedure where failed deployments can be reverted instantly using
kubectl rollout undo. -
Persistence: Fixed complex volume binding issues on EKS by implementing the AWS EBS CSI driver and custom
PGDATApathing.
Observability: The Full Stack
You can't fix what you can't see. I implemented the full LGTM stack (Loki, Grafana, Tempo, Metrics):
- Metrics: Prometheus scraping service endpoints.
- Logs: Loki aggregating distributed container logs.
- Traces: Tempo providing end-to-end request tracing.
- Visualize: Custom Grafana dashboards for a single pane of glass monitoring.
Conclusion
This project was a deep dive into the realities of cloud-native engineering. From handling stateful persistence in Kubernetes to enforcing policy-as-code, it taught me that the best systems are the ones that are both automated and transparent.
Check out the repo here: GitHub
Top comments (2)
Good work
Nice project !!!