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Infrastructure & DevOps

✅ Required: Choose your technology stack and justify each component

✅ Required: Design your containerization and orchestration strategy

✅ Required: What's your approach to monitoring, logging, and observability?

✅ Optional: How do you handle service discovery, load balancing, and health checks?

✅ Optional: Design your CI/CD pipeline and deployment strategy

✅ Optional: How do you handle rollbacks and feature flags for gradual rollouts?

1. Technology stack and justification

Chosen stack: Node.js + Kubernetes + Kafka + AWS managed services

1.1 Node.js

Non-blocking, asynchronous architecture allows handling thousands of concurrent connections efficiently, ideal for real-time collaborative workloads.

Supports cluster mode for horizontal scalability, with built-in load balancing across worker processes.

Trade-off: single-threaded nature may require careful handling of CPU-bound tasks, but for I/O-bound workloads like event processing, Node.js excels.

1.2 Kubernetes (K8s)

Container orchestration platform for scaling, self-healing, and automated deployments.

Horizontal Pod Autoscaler (HPA) automatically adjusts replicas based on CPU/memory metrics or custom metrics, supporting dynamic workloads.

Trade-off: K8s introduces operational complexity, but the benefits of resilience, rolling updates, and scaling outweigh the overhead.

1.3 Kafka

Event streaming platform for reliable asynchronous communication and durable replication across services.

Ensures decoupling of services and supports cross-region replication for failover and eventual consistency.

Trade-off: operational overhead can be mitigated using AWS Managed MSK, reducing DevOps burden.

1.4 AWS managed services

Include DynamoDB, S3, RDS, MSK, etc.

Trade-off: slightly higher cost than self-managed, but drastically reduces operational overhead and allows the team to focus on application logic and reliability.

2. Containerization and orchestration

Docker is used for containerization, ensuring consistent environments across development, testing, and production.

2.1 Kubernetes orchestrates containers, managing:

  • Deployment rollouts and rollbacks

  • Service discovery

  • Horizontal scaling via HPA

  • Self-healing through pod restarts and rescheduling

2.2 Cluster mode in Node.js:

  • Each pod runs Node.js in cluster mode, spreading worker processes across CPU cores.

  • Kubernetes Service + Ingress manages internal and external load balancing, distributing requests to multiple pods efficiently.

3. Observability, monitoring, and logging

3.1 Metrics collected:

  • Application-level: request latency, error rates, throughput

  • Infrastructure-level: pod CPU/memory usage, node health

  • Event streaming: Kafka lag and throughput

3.2 Tools:

  • Prometheus for metrics collection

  • Grafana for dashboards and visualization

  • OpenSearch (or Elasticsearch) + Fluent Bit for centralized logging

  • Fluent Bit collects logs from pods and forwards them to OpenSearch

  • OpenSearch provides searchable, structured, and durable logs, with Kibana or Grafana for visualization

  • OpenTelemetry for distributed tracing and telemetry propagation

Trade-offs: open-source stack provides flexibility and cost control, but requires proper maintenance; fully managed solutions (per example CloudWatch, Datadog) reduce maintenance but increase costs.

4. Service discovery, load balancing, and health checks

Service discovery: Kubernetes DNS-based internal service discovery, no manual configuration needed.

4.1 Load balancing:

Kubernetes Services (ClusterIP / NodePort / LoadBalancer) distribute traffic across pods.

Ingress controllers manage routing and TLS termination.

Node.js cluster mode handles intra-pod load balancing.

Health checks: Kubernetes liveness and readiness probes ensure traffic is only routed to healthy pods.

5. CI/CD pipeline and deployment strategy

CI/CD stack: GitHub Actions + ArgoCD + SonarQube

GitHub Actions triggers pipelines on commits/PRs.

ArgoCD handles declarative GitOps deployments to Kubernetes clusters.

SonarQube performs static code analysis for code quality and security vulnerabilities.

5.1 Developer experience & tooling:

  • Husky + lint-staged for pre-commit hooks

  • ESLint + Prettier for code formatting and linting

  • Jest for unit and integration tests

  • Commitlint ensures structured Git commit messages

  • Testing strategy: pipelines enforce linting, unit tests, integration tests, and code quality gates before deployment.

6. Rollbacks and feature flags

6.1 Rollbacks:

Managed via ArgoCD, allowing instant rollback to previous stable deployment.

Docker image tags and GitOps manifests provide deterministic rollback points.

6.2 Feature flags:

Implemented via GrowthBook or similar tooling.

Allows gradual rollouts, A/B testing, and instant disable without redeploying code.

Trade-off: adds operational complexity, but drastically reduces risk for production changes.