The 10 BIG Questions of System Design: A Detailed Exploration

Concept Explanation

System design interviews are a critical component of technical evaluations for software engineering roles, particularly at leading technology firms, where candidates are assessed on their ability to architect scalable, reliable, and efficient systems. The “10 BIG Questions of System Design” represent a comprehensive set of challenges frequently posed to evaluate a candidate’s depth of understanding, problem-solving skills, and ability to apply architectural principles. These questions span functional and non-functional requirements, scalability, performance, security, and resilience, requiring a structured approach to address them effectively. The core principle is to demonstrate a methodical process—clarifying assumptions, designing holistically, detailing components, evaluating trade-offs, and addressing edge cases.

This elaborated guide outlines each of the ten critical questions, providing detailed guidance on how to approach them. The methodology involves breaking down each question into sub-components, leveraging examples, and integrating practical considerations such as load estimates, latency targets, and availability goals. This approach ensures candidates can articulate a clear rationale, showcase technical expertise, and adapt to interviewer feedback, mirroring the iterative nature of system design in industry settings.

The 10 BIG Questions and Guidance

1. What are the Functional Requirements?
   • Guidance: Begin by identifying the core features the system must support. Engage the interviewer with questions to clarify scope, such as user actions (e.g., posting, searching), data types (e.g., text, images), and workflows (e.g., registration, payment). List requirements systematically—e.g., for a social media platform, include user authentication, post creation, feed generation, and notifications.
   • Detail: Specify inputs (e.g., username, password), outputs (e.g., JSON response with post ID), and interactions (e.g., API calls like /api/v1/posts). Assume a baseline scale (e.g., 1 million users) and refine based on feedback. Document edge cases (e.g., rate limits, invalid inputs) to demonstrate thoroughness.
   • Example: For a URL shortener, requirements include URL submission, unique shortcode generation, redirection, and analytics (e.g., click counts).
2. What are the Non-Functional Requirements?
   • Guidance: Address performance, scalability, availability, and security. Ask about latency targets (e.g., < 200ms), throughput (e.g., 10k requests/second), uptime (e.g., 99.9
   • Detail: Consider constraints like geographic distribution (e.g., multi-region support) or compliance (e.g., GDPR). Propose SLAs (e.g., 99.95
   • Example: For a ride-sharing app, target < 5-second matching latency, 99.9
3. How Will You Estimate the Scale?
   • Guidance: Estimate system load based on user base, request patterns, and data volume. Use back-of-the-envelope calculations—e.g., 1 million daily users, 10 requests/user/day = 10 million requests/day or ~115 requests/second. Factor in peaks (e.g., 10x during sales).
   • Detail: Break down storage (e.g., 1KB/user profile, 1TB for 1M users), bandwidth (e.g., 1Mbps/stream for video), and compute (e.g., 1 CPU core/1000 req/s). Validate assumptions with the interviewer and adjust for growth (e.g., 2x in 6 months).
   • Example: A news site with 5M users, 5 page views/user/day, and 100KB/page = 2.5TB/day traffic.
4. What is the High-Level Design?
   • Guidance: Sketch a block diagram with major components—clients, backend services, databases, caches, queues, and external APIs. Define data flow (e.g., request → service → database → response) and justify choices (e.g., microservices for modularity).
   • Detail: Include load balancers (e.g., NGINX), CDNs (e.g., Cloudflare), and regional deployment for latency. Highlight separation of concerns (e.g., authentication service vs. business logic).
   • Example: For a chat app, design includes client (mobile), backend (WebSocket server), database (Redis for messages), and CDN for media.
5. How Will You Design the APIs?
   • Guidance: Propose RESTful or GraphQL APIs with clear endpoints, methods, and payloads. Define request/response formats (e.g., JSON) and error handling (e.g., 404, 429). Consider versioning (e.g., /v1/) for future changes.
   • Detail: Use tools like Swagger for documentation. Implement rate limiting (e.g., 100 req/min) and authentication (e.g., OAuth). Optimize for performance (e.g., pagination for large datasets).
   • Example: For a URL shortener, design /api/v1/shorten (POST {url}) returning {shortcode}, with /api/v1/redirect/{shortcode} for redirection.
6. What Database Would You Use and Why?
   • Guidance: Choose between SQL (e.g., PostgreSQL for structured data) and NoSQL (e.g., MongoDB for unstructured) based on requirements. Discuss trade-offs—consistency vs. scalability, schema rigidity vs. flexibility.
   • Detail: Propose indexing (e.g., B-tree for queries), sharding (e.g., by user ID), and replication (e.g., master-slave) for high availability. Consider caching (e.g., Redis) to offload reads.
   • Example: A social media feed uses Cassandra for scalability and eventual consistency, with PostgreSQL for user accounts.
7. How Will You Ensure Scalability?
   • Guidance: Outline horizontal scaling (add servers) and vertical scaling (upgrade hardware). Discuss load balancing, caching, and database sharding. Propose auto-scaling policies (e.g., scale out at 80
   • Detail: Use CDNs for static content, message queues (e.g., Kafka) for task offloading, and microservices for independent scaling. Test with load tools (e.g., JMeter) to validate capacity.
   • Example: An e-commerce site scales horizontally with 10 servers during Black Friday, using Redis to cache product listings.
8. How Will You Handle Fault Tolerance and Reliability?
   • Guidance: Address failures with redundancy (e.g., multi-region deployment), failover (e.g., standby servers), and monitoring (e.g., Prometheus). Define recovery objectives—RPO (e.g., 1 minute data loss) and RTO (e.g., 5 minutes recovery).
   • Detail: Implement circuit breakers for degraded services, backups (e.g., S3), and health checks. Plan for network partitions with CAP theorem trade-offs (e.g., AP over CP).
   • Example: A payment system uses replicated databases and failover clusters, targeting 99.99
9. What Are the Security Considerations?
   • Guidance: Cover authentication (e.g., JWT), authorization (e.g., RBAC), data encryption (e.g., TLS), and input validation. Address threats like DDoS, SQL injection, and data breaches.
   • Detail: Use rate limiting (e.g., 1000 req/s), WAF (e.g., AWS WAF), and audit logs. Comply with standards (e.g., PCI-DSS for payments) and test with penetration tools.
   • Example: A healthcare app encrypts patient data with AES-256 and restricts access via OAuth scopes.
10. How Will You Test and Optimize the System?
    • Guidance: Propose testing strategies—unit tests, integration tests, load tests—and optimization techniques—caching, indexing, query optimization. Define KPIs (e.g., latency < 100ms, throughput 5k req/s).
    • Detail: Use tools like Postman for API testing, JMeter for load, and profiling (e.g., New Relic) for bottlenecks. Iterate based on metrics, adjusting cache hit rates (> 80
    • Example: A video streaming service tests with 1M concurrent users, optimizing CDN delivery to reduce latency to < 200ms.

Real-World Example: Designing a URL Shortener

This solution addresses the design of a URL shortener system based on the provided requirements and questions, aligning with real-world engineering practices. The approach integrates functional and non-functional requirements, scalability estimates, architectural design, and optimization strategies to deliver a robust system. The methodology ensures clarity by detailing each component, justifying trade-offs, and proposing a verifiable implementation, suitable for a system design interview or production environment.

Real-World Example: Designing a URL Shortener

Q1-2: Requirements and Non-Functional Goals

• Functional Requirements: The system must support URL shortening (converting long URLs to shortcodes), redirection (resolving shortcodes to original URLs), and click analytics (tracking redirection counts and user metadata).
• Non-Functional Requirements: Achieve latency below 100 milliseconds for all operations, maintain 99.9

Q3: Scale Estimation

• Traffic Estimation: With 1 million daily URL submissions and 200,000 redirects, total requests are 1.2 million/day. This translates to approximately 14 requests/second on average (1.2M / 86,400 seconds). Peak load could reach 10x during surges (e.g., 140 req/s).
• Storage Estimation: Assume each URL entry (shortcode, original URL, creation timestamp) requires 256 bytes, and analytics (click count, IP, timestamp) adds 128 bytes per redirect. For 1M URLs and 200k redirects, storage needs are approximately 256MB (URLs) + 25.6MB (redirects) = 281.6MB, rounded to 1GB with overhead for indexes and logs.
• Compute Estimation: Each request requires ~1ms CPU time for hashing and lookup, necessitating ~14 CPU cores at peak, scalable with load.

Q4: High-Level Design

• Components:
  • Web Client: A browser-based or mobile interface for users to submit URLs, built with HTML/CSS/JavaScript.
  • API Server: A backend service handling requests, implemented with Node.js for asynchronous processing, deployed on a cloud platform (e.g., AWS EC2).
  • Redis: An in-memory data store for mapping shortcodes to original URLs, ensuring low-latency lookups.
  • MySQL: A relational database for storing analytics data (e.g., click counts, timestamps) with structured querying capabilities.
• Data Flow: User submits URL via web client → API server generates shortcode, stores in Redis and logs analytics in MySQL → Redirect requests resolve shortcodes via Redis → Analytics updated in MySQL.

Q5: API Design

• Endpoints:
  • /api/v1/shorten (POST): Accepts { “url”: “https://example.com/long/url” }, returns { “shortcode”: “abc123”, “short_url”: “https://short.ly/abc123” } with 201 status.
  • /r/{shortcode} (GET): Resolves abc123 to the original URL, redirects with 301, and logs analytics.
• Specifications: Use JSON for payloads, implement versioning (/v1/), and return errors (e.g., 400 for invalid URLs, 429 for rate limits).
• Implementation: Validate URLs with regex (e.g., ^(https?://)), generate shortcodes with base62 encoding of a incrementing ID.

Q6: Database Design

• Redis: Stores key-value pairs (shortcode → original URL, TTL of 30 days). Configured for high availability with replication, optimized for < 1ms lookups.
• MySQL: Schema includes:
  • urls table: { id (PK), shortcode, original_url, created_at }
  • analytics table: { id (PK), shortcode (FK), click_count, ip_address, timestamp }
  • Indexed on shortcode for fast joins, with partitioning by date for analytics queries.

Q7: Scalability Strategy

• Load Balancers: Deploy NGINX to distribute traffic across API servers, using round-robin or least-connections algorithms.
• Sharding: Partition Redis by shortcode range (e.g., a-m, n-z) across nodes, with consistent hashing to minimize remapping. Scale MySQL with read replicas for analytics and sharding by created_at for write distribution.
• Auto-Scaling: Configure AWS Auto Scaling to add instances when CPU exceeds 70

Q8: Fault Tolerance and Reliability

• Redis Replication: Use Redis Sentinel for automatic failover, with a master-slave setup across availability zones.
• Backup to S3: Schedule daily snapshots of MySQL and Redis data to AWS S3, with a retention policy of 30 days. Restore time objective (RTO) < 10 minutes, recovery point objective (RPO) < 1 minute.
• Monitoring: Implement health checks with Prometheus, alerting on downtime > 1

Q9: Security Considerations

• HTTPS: Enforce TLS 1.3 with a certificate from Let’s Encrypt, encrypting all traffic.
• Rate Limiting: Apply 100 requests/minute per IP using an API gateway (e.g., AWS API Gateway), with a 429 response for exceedance.
• Input Validation: Sanitize URLs to prevent injection (e.g., strip malicious scripts), reject non-HTTP/HTTPS schemes.

Q10: Testing and Optimization

• Testing: Use JMeter to simulate 1 million requests/day, with 140 req/s peaks, validating latency (< 100ms) and uptime (99.9
• Optimization: Target > 90
• Metrics: Monitor p99 latency, throughput (14 req/s average), and cache efficiency with Grafana dashboards.

Implementation Considerations

• Deployment: Use Docker containers on Kubernetes, with CI/CD via Jenkins for automated builds and deployments.
• Maintenance: Schedule maintenance during low-traffic windows (e.g., 2 AM IST), with rolling updates to minimize downtime.
• Cost Management: Estimate $500/month for EC2 (2 m5.large instances), $50 for RDS, and $20 for S3, optimizing with reserved instances.

Trade-Offs and Strategic Decisions

• Latency vs. Throughput: Prioritize < 100ms latency with Redis caching, accepting reduced throughput (e.g., 100 req/s per node) during peaks, mitigated by scaling.
• Consistency vs. Availability: Use eventual consistency for analytics (5-second lag acceptable), ensuring 99.9
• Cost vs. Performance: Opt for Redis over in-memory SQL for speed, despite higher licensing costs, justified by user experience gains.