1. The Question
Describe the end-to-end design and architecture of the enterprise program you managed. Explain major components, data flow, technology choices (frontend, backend, databases, messaging, infra, monitoring), and the reasoning and trade-offs behind those choices.
2. Clarifying Questions
- What are the primary business capabilities (e.g., customer management, data processing, reporting)?
- Expected traffic patterns: peak QPS, daily active users, batch jobs?
- SLAs: availability, RTO/RPO for disasters?
- Compliance/security/regulatory constraints (e.g., PII, encryption requirements)?
- Team constraints: languages/skills, ops maturity?
3. Requirements
Functional:
- Customer management: CRUD, search, history
- Secure data processing pipelines for analytics and reporting
- Admin tools and dashboards
Non-functional:
- High availability (>=99.9%) and fault isolation
- Horizontal scalability for web and processing workloads
- Low latency for user-facing APIs (sub-200ms typical)
- Strong security: OAuth2/JWT, encryption at rest/in transit
- Operability: observability, automated deploys, rollbacks
Constraints:
- Enterprise adoption, existing Java backend skillset, AWS hosting preference.
4. Scale Estimates
Example targets used to size components:
- 100k monthly active users, 5k concurrent users
- API peak: 500 QPS, average 150 QPS
- Background jobs: process 1M records/day; some CPU-bound ML/data transforms
- Datastore: tens of GBs to low TBs depending on retention
These guided choices for caching, read replicas, async processing, and autoscaling policies.
5. Data Model
Relational (PostgreSQL):
- Core entities: users, customers, accounts, transactions
- Normalized schemas for strong integrity, foreign keys, ACID transactions
- Use partitioning for high-volume tables (time- or tenant-based)
Document store (MongoDB):
- Flexible documents for semi-structured customer profiles, audit trails, or integrations where schema evolves
Caching (Redis):
- Session data, hot lookups, rate-limiting counters, and materialized views to improve read latency
Storage (S3):
- Blob storage for reports, batch outputs, and archived datasets with lifecycle policies
6. API Design
Principles:
- RESTful JSON APIs with resource-oriented endpoints for core features
- Versioning strategy: /v1/, /v2/ path versions
- Authentication: OAuth 2.0 with JWT access tokens; short-lived tokens and refresh tokens
- Idempotent design for critical operations (client-provided request IDs for retries)
- Rate limiting at API gateway and per-user quotas
Example endpoints:
- POST /v1/customers
- GET /v1/customers/{id}
- GET /v1/customers?search=...
- Webhooks and event publishing endpoints for external integrations
7. High-Level Architecture
Overview (Markdown diagram-like description):
- Clients (React + TypeScript SPA) talk to an API Gateway / Load Balancer.
- API Gateway routes to microservices implemented in Spring Boot (Java) for transactional APIs and FastAPI (Python) for compute-heavy services.
- Services persist to PostgreSQL and MongoDB as appropriate; Redis used for caching.
- Async events are published to Kafka (for high-throughput event streaming) and RabbitMQ (task queue patterns where order/ack semantics matter).
- Batch and stream processing consumers (Python) subscribe to Kafka for analytics and ETL.
- All services run containerized on Docker and orchestrated by Kubernetes (EKS).
- CI/CD: GitLab pipelines build containers, run tests, push images to a registry, and deploy with Helm charts.
- Observability: Prometheus (metrics) + Grafana dashboards; ELK stack for centralized logs.
- Storage: S3 for artifacts and archival; RDS for managed PostgreSQL; managed MongoDB or self-hosted in k8s depending on requirements.
8. Detailed Design Decisions
Frontend: React + TypeScript
- Reason: SPA UX, reusable component model, type safety reduces runtime bugs and improves dev ergonomics.
Backend: Spring Boot + FastAPI
- Spring Boot for primary transactional services due to Java ecosystem, mature security, and existing team expertise.
- FastAPI (Python) for CPU- or I/O-bound data processing where rapid iteration and Python libraries (data/ML) are beneficial.
Microservices
- Independent deploys, fault isolation, and language heterogeneity.
- Trade-offs: increased operational complexity and cross-service coordination.
Databases
- PostgreSQL for transactional integrity and complex queries.
- MongoDB for flexible document storage where schema changes are frequent.
- Redis to reduce latency and DB load via caching.
Messaging: Kafka + RabbitMQ
- Kafka for high-throughput event streaming, retention, and replayability (analytics pipelines).
- RabbitMQ for task queues, requiring acknowledgements and fine-grained routing.
Containerization & Orchestration
- Docker + Kubernetes (EKS) for consistent environments, autoscaling, and deployment patterns.
Cloud: AWS
- Native services (EC2, S3, RDS, IAM) for cost and operational efficiencies.
CI/CD: GitLab CI + Helm
- Automated testing/building, and Helm templating for environment-specific deploys.
Monitoring & Logging
- Prometheus/Grafana for metrics and alerting; ELK for search-able logs and forensic debugging.
Security
- OAuth2 + JWT for stateless auth, TLS everywhere, KMS for secrets and encryption keys.
9. Bottlenecks & Scaling
Potential bottlenecks and mitigation:
- Database write hotspots: use write sharding, partitioning, or CQRS (writes to primary, reads from read replicas).
- Long-running processing tasks: move to async workers with backpressure via Kafka and autoscaled worker pools.
- Inter-service latency: use circuit breakers, bulkheads, and client-side timeouts; colocate services where low latency required.
- Kafka/RabbitMQ throughput: partition tuning, consumer group scaling, and monitoring lag.
- Cold starts or burst traffic: use horizontal pod autoscaling, warm pools for VMs, and caching layers.
- Operational complexity: invest in runbooks, automation, and observable dashboards; use managed services where sensible.
10. Follow-up Questions / Extensions
- How would you migrate a monolith into this microservices setup incrementally?
- How to design tenancy and multi-tenant data isolation?
- How to implement multi-region failover and data replication with minimal RPO?
- What cost-optimization strategies would you apply on AWS for this architecture?
- How to add real-time features (websockets, push notifications) while preserving scalability?
11. Wrap-up
The chosen architecture balances developer productivity and operational resilience: Spring Boot for enterprise-grade transactional services, FastAPI for data/compute workloads, PostgreSQL and MongoDB for structured and flexible storage, Kafka/RabbitMQ for decoupled communication, and Kubernetes on AWS for scalable deployments. Key trade-offs include operational complexity vs. scalability and flexibility. Prioritize observability, automated CI/CD, and incremental rollout to manage risks.