Answer for General Backend Engineering Interview Questions
Backend System Design interview questions.
1. System Design Questions
• URL Shortening Service:
Use Base62 for short codes, a NoSQL DB like Redis for mapping, caching for popular URLs, and an API Gateway for traffic management. Handle collisions and expired URLs carefully.
• Messaging System:
Use WebSockets for real-time updates, Kafka for queuing, and Cassandra for storing messages. Implement push notifications and ensure horizontal scalability with sharded queues.
• Rate-Limiting for APIs:
Use Redis with sliding window or token bucket algorithms. Enforce limits at the API Gateway. Handle IP spoofing and sudden bursts dynamically.
• Notification Service:
Use Kafka for queuing, workers for channel-specific notifications, and services like FCM for push. Store user preferences in relational DBs and retry failures with a dead-letter queue.
• Search Engine Backend:
Use crawlers, Lucene/Elasticsearch for indexing, and cache frequent queries in Redis. Implement ranking with TF-IDF or ML models for personalization.
2. Database and Storage
• SQL vs. NoSQL:
SQL for structured, transactional data; NoSQL for unstructured, scalable data.
E.g., SQL for banking, NoSQL for social media.
• E-commerce Schema:
Normalize tables for products, orders, and customers. Use indexes for quick lookups.
• Sharding:
Split data across servers by key (e.g., user ID). Useful for scaling write-heavy apps.
• Transactions and ACID:
Ensure atomicity, consistency, isolation, durability. Critical for financial systems.
• Long Query Handling:
Add indexes, optimize queries, analyze execution plans, or split large operations.
3. API Development
• RESTful API:
CRUD for resources like blogs/posts. Use clear routes and JSON responses.
• REST vs. GraphQL:
REST for simplicity; GraphQL for flexibility in querying complex data.
• API Versioning:
Use path-based (e.g., /v1/resource) or header-based approaches.
• API Security:
Use HTTPS, auth tokens, input validation, and rate-limiting.
• Idempotency:
Ensure HTTP methods like PUT/DELETE are safe for retries to avoid duplicate changes.
4. Performance and Scalability
• Optimize Latency:
Profile bottlenecks, implement caching, use CDNs, and parallelize heavy tasks.
• Traffic Spikes:
Auto-scale infrastructure, use load balancers, and implement backpressure mechanisms.
• Caching:
Use Redis/Memcached for fast access. Cache queries and static content.
• Scaling:
Vertical scaling = bigger machines; horizontal scaling = more machines. Horizontal is better for distributed systems.
• Memory Leaks:
Use profiling tools, monitor objects, and clean unused references.
5. Microservices and Distributed Systems
• Monolithic vs. Microservices:
Monolith = single codebase; microservices = modular, independent services.
• Inter-Service Communication:
Synchronous (HTTP/gRPC) for low latency; asynchronous (Kafka) for scalability.
• Distributed Transactions:
Use two-phase commits or eventual consistency (e.g., Sagas).
• Service Discovery:
Use tools like Consul or Eureka for locating services dynamically.
• Eventual Consistency:
Accept temporary inconsistencies for high scalability (e.g., in NoSQL systems).
6. Concurrency and Threading
• Handle Concurrent Requests:
Use thread pools, async programming, and rate-limiting.
• Multithreading vs. Async:
Multithreading = parallel execution; async = efficient I/O handling.
• Race Conditions:
Use locks, semaphores, or atomic variables to prevent.
• Thread-Safe Structures:
Use Python’s threading.Lock or collections like queue.Queue.
• Process vs. Thread:
Processes = isolation; threads = shared memory. Use processes for heavy tasks.
7. Testing and Debugging
• Unit Tests:
Test individual API methods using frameworks like Pytest.
• Integration Tests:
Validate end-to-end workflows involving multiple components.
• Troubleshooting 500 Errors:
Check logs, trace errors, and simulate requests to identify issues.
• Logging in Microservices:
Centralize logs with tools like ELK or Grafana.
• Mocks in Testing:
Replace real dependencies with simulated ones to test in isolation.
8. Security and Compliance
• Authentication and Authorization:
Use OAuth, JWT, or sessions.
• SQL Injection Prevention:
Use parameterized queries and ORM libraries.
• Data Encryption:
Encrypt in transit (TLS) and at rest (AES).
• CORS:
Restrict origins to prevent unauthorized browser requests.
• Sensitive Data Handling:
Store API keys in vaults and use environment variables.
9. DevOps and CI/CD
• CI/CD Pipeline:
Automate builds, tests, and deployments using GitHub Actions, Jenkins, etc.
• Containerization:
Use Docker to package apps. Share dependencies across environments.
• Kubernetes:
Manage scaling, deployment, and service recovery for microservices.
• Zero-Downtime Deployment:
Use blue-green or rolling deployments.
• Service Monitoring:
Use Prometheus and Grafana for real-time health checks.
Scenario-Based Questions
• Debugging Slow Service:
Profile resources, analyze database queries, check API latencies, and scale bottlenecks.
• API Failure Handling:
Use retries, circuit breakers, and fallbacks to handle intermittent failures.
• Live DB Migration:
Use tools like Flyway. Migrate in phases and run read replicas during cutovers.
