Case Study: How to Design Netflix, Uber, or Instagram in System Design Interviews

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30+ Real Interview Questions and In-Depth Answers

Based on real questions from FAANG interviews (sourced from LeetCode, Reddit, and expert sites), here are 35 in-depth questions with answers. I’ve categorized them by app for clarity. These go beyond basics, incorporating trade-offs and optimizations.

Netflix-Related Questions (12)

1. Design a scalable CDN like Netflix’s Open Connect.
   Answer: Use hierarchical OCAs (central, regional, edge). Cache with LRU/LFU; auto-scale via Kubernetes. Trade-off: Cost vs. latency—edge servers reduce delay but increase expenses. Handles 95B hours of viewing.
2. Design a fault-tolerant streaming service.
   Answer: Replicate across regions with Cassandra/DynamoDB. Use load balancers and heartbeats for failover. Adaptive streaming (HLS) for resilience. Trade-off: Strong consistency vs. availability—opt for eventual via caching.
3. Design a personalized recommendation system.
   Answer: Collect data via Kafka; use collaborative filtering + ML (matrix factorization). Scale with Spark; personalize 80% of views. Trade-off: Real-time vs. batch—hybrid for accuracy.
4. Design a low-latency streaming service.
   Answer: Chunk videos; use DASH for bitrate adaptation. CDN precaching based on patterns. Latency budget: <200ms. Trade-off: Quality vs. speed—compress for poor networks.
5. Design an advanced search with autocomplete.
   Answer: Trie for prefixes; Elasticsearch for ranking. Personalize via user history. Scale with sharding. Trade-off: Freshness vs. cost—daily indexing.
6. Design a video streaming service like Netflix.
   Answer: Encode videos in multiple formats; use CDNs. Handle 280M users with microservices. Trade-off: Storage vs. quality—transcode on-demand.
7. Design a scalable metadata storage system.
   Answer: Cassandra for NoSQL; index with Elasticsearch. Replicate for HA. Trade-off: Read speed vs. write—use eventual consistency.
8. Design a real-time analytics system.
   Answer: Ingest with Kafka; process via Spark. Dashboards with Grafana. Trade-off: Real-time vs. accuracy—sample data.
9. Design a scalable logging and monitoring system.
   Answer: ELK stack (Elasticsearch, Logstash, Kibana). Alert with Prometheus. Scale distributed. Trade-off: Retention vs. cost—tiered storage.
10. Design a high-availability distributed database.
    Answer: Use CockroachDB for SQL with replication. Sharding by title. Trade-off: CAP—prioritize AP for global access.
11. Design a global load balancing system.
    Answer: Anycast DNS; GSLB for geo-routing. Failover with health checks. Trade-off: Latency vs. cost—multi-cloud.
12. Design a user profile management system.
    Answer: Store in DynamoDB; sync via Kafka. Privacy with GDPR compliance. Trade-off: Security vs. speed—encrypt at rest.

Uber-Related Questions (12)

1. Design a ride-sharing system like Uber.
   Answer: Geospatial indexing (S2/QuadTree) for matching. WebSockets for real-time. Handle 3B trips/Q. Trade-off: Accuracy vs. speed—approximate nearest.
2. Design a real-time location-tracking system.
   Answer: GPS pings to Kafka; store in Redis. Query with Elasticsearch Geo. Trade-off: Battery vs. precision—sample every 10s.
3. Design a notification system.
   Answer: FCM for push; Kafka queue. Handle failures with retries. Trade-off: Delivery vs. spam—rate limit.
4. Design a payment system.
   Answer: Stripe integration; ACID with PostgreSQL. Fraud ML. Trade-off: Security vs. speed—async processing.
5. Design a distributed logging system.
   Answer: Fluentd collectors; store in S3. Query with Athena. Trade-off: Volume vs. cost—compress logs.
6. Design a system for surge pricing.
   Answer: ML on supply/demand; update via Redis pub/sub. Trade-off: Fairness vs. revenue—cap surges.
7. Design a scalable logging and monitoring system.
   Answer: Prometheus + Grafana; logs to ELK. Auto-alerts. Trade-off: Granularity vs. overhead—sample metrics.
8. How would you handle distributed transactions in Uber?
   Answer: SAGAs for microservices; eventual consistency. Trade-off: Speed vs. atomicity—compensating actions.
9. Design ETA calculation.
   Answer: Graph algorithms (Dijkstra) on maps; ML for traffic. Cache routes. Trade-off: Real-time vs. compute—precompute grids.
10. Design driver matching with constraints.
    Answer: Filter by rating/vehicle; optimize with Hungarian algorithm. Trade-off: Wait time vs. quality—thresholds.
11. Handle scalability for 50k concurrent users.
    Answer: Auto-scale EC2; shard by city. Trade-off: Cost vs. peaks—predictive scaling.
12. Design fraud detection in payments.
    Answer: Anomaly ML on transactions; rules engine. Trade-off: False positives vs. losses—tune thresholds.

Instagram-Related Questions (11)

1. Design Instagram’s feed generation.
   Answer: Fan-out for small users; pull for influencers. Cache in Redis. Handle 500M DAU. Trade-off: Write heavy vs. read—hybrid.
2. Design photo/video upload service.
   Answer: S3 storage; transcode with FFmpeg. Chunk large files. Trade-off: Quality vs. size—compress.
3. Design search with hashtags/locations.
   Answer: Elasticsearch inverted index. Geo-sharding. Trade-off: Relevance vs. speed—BM25 ranking.
4. Design like/comment system.
   Answer: Counters in Cassandra; notifications via Kafka. Trade-off: Consistency vs. scale—eventual.
5. Design follow/unfollow mechanism.
   Answer: Graph DB (Neo4j) for social graph. Update feeds async. Trade-off: Immediate vs. delayed—queue jobs.
6. Handle 60M daily uploads.
   Answer: Distributed queues; shard storage. Trade-off: Durability vs. speed—replicate.
7. Design stories feature.
   Answer: Ephemeral storage; TTL in Redis. Push to followers. Trade-off: Privacy vs. virality—view tracking.
8. Design Instagram overall.
   Answer: Microservices; GraphQL federation. Scale to 2B users. Trade-off: Monolith simplicity vs. modularity.
9. Optimize for low latency feeds.
   Answer: Precompute feeds; CDN for media. Trade-off: Freshness vs. load—poll periodically.
10. Design analytics for engagement.
    Answer: Kafka streams; store in BigQuery. Trade-off: Real-time vs. batch—hybrid.
11. Handle multi-tenant privacy.
    Answer: Row-level security in DB; encrypt data. Trade-off: Access speed vs. compliance—audit logs.

These questions are drawn from real interviews at Meta, Google, and Netflix, emphasizing depth.

Tips for Acing Your Interview

• Practice with mocks; use frameworks like RADIO (Requirements, API, Data, Infrastructure, Optimization).
• Ask clarifying questions: “What’s the QPS? Any geo-constraints?”
• Draw diagrams; explain trade-offs verbally.
• For quick prep, join our crash course.

As one Reddit user shared, “System design is easy: Understand databases, optimizations, and practice with ChatGPT.” Stay curious—practice builds confidence.

In conclusion, designing these apps teaches scalable thinking. Apply these insights, and you’ll shine. Ready to practice? Dive into our resources today.

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