The question sounds manageable until you realize how many sub-problems are hiding inside it.
Start by clarifying functional and non-functional requirements, then propose a high-level architecture with clear separation of concerns (e.g., services for auth, social graph, posts, feed, notifications). Dive into data modeling and trade-offs for each component, emphasizing scalability, availability, and consistency choices aligned with Amazon's leadership principles.
Pro tip: Explicitly call out trade-offs (e.g., fan-out on write vs. read for the feed) and tie decisions to business needs like low-latency reads or cost efficiency. Mention how you'd evolve the design as scale grows, showing foresight.
Ask questions to define core features, scale (users, posts, QPS), latency/consistency needs, and constraints. Prioritize must-haves vs. nice-to-haves.
Sketch major components (API gateway, auth service, user service, social graph service, post service, feed service, notification service) and their interactions. Choose appropriate storage (SQL vs. NoSQL) per service.
Design schemas for users, follows, posts, comments, likes, and notifications. Define key API endpoints and data flows for posting, following, and fetching the feed.
Discuss partitioning, caching, fan-out strategies for the feed, and consistency models. Explain how to handle hot users, read-heavy workloads, and eventual consistency for notifications.
Mention observability, failure handling, and future improvements (e.g., ML-based ranking). Summarize key decisions and their rationale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a pretty standard relational layout for users and follows, then proposed a separate feed table or cache layer.
Start by clarifying the scale and access patterns (read-heavy feed, write-heavy follow graph) and the consistency requirements. Then propose a data model that separates the feed (e.g., fan-out on write with a timeline cache) from the follow graph (e.g., adjacency list with composite keys). Finally, detail the primary keys, secondary indexes, and sharding strategy to support efficient lookups and range scans.
Pro tip: Emphasize that index design must be driven by query patterns, not just data shape—show how you'd validate with back-of-the-envelope calculations and mention trade-offs like denormalization for read performance vs. write amplification.
Ask about scale (users, follows, feed reads/writes per second), latency SLAs, and consistency needs (e.g., eventual consistency for feed). Identify the top queries: get feed for user, get followers/followees, check if A follows B.
Propose a table with composite primary key (follower_id, followee_id) to ensure uniqueness and support efficient lookups of who a user follows. Add a secondary index on (followee_id, follower_id) to answer 'who follows me' queries.
Use a timeline table keyed by (user_id, post_id) or (user_id, timestamp) to store precomputed feed entries. Consider fan-out on write for active users and fan-out on read for celebrities to balance write and read costs.
For the follow graph, shard by follower_id to distribute writes; for the feed, shard by user_id to localize reads. Use secondary indexes (e.g., on post_id for likes/comments) and consider covering indexes to avoid extra lookups.
Address hot partitions (e.g., celebrity followees), caching (Redis for feed), and denormalization (storing post metadata in feed). Mention how you'd handle deletes/unfollows and ensure idempotency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Kept it high level, REST-style endpoints, talked through request and response shapes.
Start by clarifying requirements and constraints (scale, read/write ratio, consistency needs) before diving into each feature. Then walk through each API endpoint, covering HTTP methods, paths, request/response schemas, status codes, and key design decisions like authentication, pagination, and idempotency. Finally, discuss trade-offs and how the design supports Amazon's scale and operational excellence.
Pro tip: Emphasize idempotency and pagination for feed and notifications, and mention how you'd version APIs to avoid breaking changes—Amazon values backward compatibility and customer trust.
Ask about scale (users, QPS), read/write patterns, latency SLAs, and consistency requirements. This shows you don't jump to solutions without understanding the problem.
Outline endpoints for sign-up, login, token refresh, and logout. Discuss token strategy (JWT vs. opaque), secure storage, and rate limiting.
Define endpoints for creating, updating, and deleting posts. Cover request validation, idempotency keys, and media handling (e.g., pre-signed URLs).
Specify endpoints for fetching a user's feed with pagination (cursor-based), filtering, and sorting. Discuss caching and fan-out strategies for scalability.
Describe endpoints for listing, marking as read, and managing notification preferences. Include push vs. pull models and delivery guarantees.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and read/write ratio, then propose a layered caching and read-replica strategy to handle high read volume. Discuss specific failure modes like cache stampedes, hot keys, and replica lag, and explain how you'd mitigate them with techniques like request coalescing and circuit breakers.
Pro tip: Tie every scaling decision back to a measurable trade-off (e.g., consistency vs. latency) and mention how you'd monitor and alarm on the failure modes you identify, showing operational maturity.
Ask about expected read QPS, data size, latency SLOs, and consistency requirements to ground your design in realistic numbers.
Propose a multi-layer approach: CDN for static assets, application-level caching (e.g., Redis) for feed data, and read replicas for the database to distribute load.
Enumerate critical failure scenarios such as cache stampede, hot keys, replica lag, cache penetration, and network partitions, and explain their impact.
Describe mitigation strategies (e.g., request coalescing, cache warming, circuit breakers, fallbacks) and how you'd monitor and alert on these failures.
Conclude by discussing the trade-offs between consistency, availability, and latency, and how you'd iterate based on metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Answered that auth and follow-state changes need to be strongly consistent because acting on stale follow data could cause real user-facing bugs.
Start by clarifying the system's core requirements and user expectations, then map data entities to consistency needs based on business impact and access patterns. For each entity, justify strong vs. eventual consistency by weighing correctness, latency, availability, and cost trade-offs, and propose a hybrid approach where appropriate.
Pro tip: Anchor your answer in Amazon's leadership principles: insist on the highest standards for correctness where it matters (e.g., payments), but bias for action and frugality elsewhere by embracing eventual consistency to scale. Always quantify the impact of inconsistency (e.g., 'a stale like count is acceptable, but a double-charged customer is not').
Ask about the system's purpose, scale, latency SLAs, and user expectations to ground consistency decisions in real requirements.
List the main entities and transactions (e.g., orders, inventory, user profiles, analytics) and classify them by business impact if inconsistent.
For each entity, choose strong or eventual consistency and explain why, referencing trade-offs like CAP theorem, latency, and cost.
Describe how to combine both models (e.g., strong for writes, eventual for reads via caching or read replicas) and handle edge cases like conflict resolution.
Walk through what happens during network partitions or node failures to show your choices maintain correctness where needed and availability elsewhere.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.