The scope of this thing is massive and I burned too much time on the tweet storage schema before they nudged me toward timelines.
Start by clarifying requirements and scale, then design the data model and APIs, followed by the high-level architecture. Focus on the core features and discuss trade-offs, especially for the home timeline generation (fan-out on write vs. read).
Pro tip: Demonstrate awareness of the read-heavy nature of social feeds and propose a hybrid approach for timeline generation, leveraging caching and precomputation for active users while handling celebrities differently.
Clarify functional and non-functional requirements, including expected scale (users, tweets, reads/writes per second) and latency goals.
Define the core entities (User, Tweet, Follow, Like, Retweet, Reply) and their relationships, and sketch the key API endpoints for each feature.
Outline the main components: load balancers, application servers, databases (SQL/NoSQL), caches, and message queues. Explain how they interact.
Discuss approaches for home and profile timelines: fan-out on write vs. fan-out on read, and how to handle celebrities and inactive users.
Analyze trade-offs (consistency vs. availability, latency vs. cost) and propose optimizations like caching, sharding, and denormalization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what is the timeline (e.g., user feed, order history), expected read QPS, latency SLA, and consistency needs. Then propose a multi-layered caching strategy with aggressive CDN/edge caching, a distributed cache like Redis, and a read-optimized datastore, while discussing trade-offs around consistency, cost, and complexity.
Pro tip: Emphasize that at this scale, caching is not just an optimization but a fundamental architectural component; mention that you would design for cache invalidation and stampede protection from day one, as these are common pitfalls.
Ask about the timeline's data size, read/write ratio, latency target (e.g., p99 < 100ms), consistency requirements (strong vs eventual), and budget. This ensures the solution aligns with business needs.
Propose caching at multiple levels: CDN for static/immutable content, edge caching for personalized but cacheable data, and a distributed in-memory cache (e.g., Redis) for dynamic timelines. Discuss cache eviction policies and TTLs.
Select a database that scales reads horizontally, such as a NoSQL store (e.g., Cassandra, DynamoDB) or a read replica setup with a relational DB. Consider denormalization and precomputed timelines for fast access.
Explain how to handle cache invalidation (e.g., write-through, write-behind, or event-driven invalidation) and trade-offs between consistency and latency. Mention techniques like versioning or time-based invalidation.
Cover horizontal scaling of cache and database layers, sharding, replication, and handling cache failures (e.g., circuit breakers, fallbacks). Also mention monitoring and load testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said adjacency list in a relational table with indexes on both follower and followee columns.
Start by clarifying requirements (scale, read/write ratio, latency, consistency) and then propose a hybrid storage model: a graph store for relationships and a denormalized timeline cache for fast reads. Explain how follow/unfollow operations update both stores asynchronously, and how timeline generation uses fan-out on write or read based on user activity.
Pro tip: Emphasize the trade-off between fan-out on write (push) and fan-out on read (pull), and suggest a hybrid approach for celebrity users to avoid write amplification. Also mention using a graph database like Neo4j for relationship queries and a wide-column store like Cassandra for timeline storage to handle scale.
Ask about the number of users, average follows per user, read/write ratio, and latency requirements. This determines whether to optimize for reads or writes.
Propose a graph representation (e.g., adjacency lists) stored in a scalable database. Discuss using a graph DB (Neo4j) or a relational DB with proper indexing, and how to handle follow/unfollow operations efficiently.
Explain fan-out on write (precompute timelines) vs fan-out on read (compute on demand). Discuss hybrid approach for celebrities and how to store timelines (e.g., Redis, Cassandra).
Describe how a follow/unfollow updates the graph and triggers timeline updates. Discuss consistency (eventual vs strong) and idempotency.
Discuss sharding, caching, and handling hot users. Mention monitoring and potential bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scope of search and trending topics in the context of DoorDash's platform, then outline a high-level architecture that addresses both. Discuss trade-offs between different technologies and approaches, emphasizing scalability, latency, and relevance.
Pro tip: Demonstrate awareness of DoorDash's specific use cases, such as searching for restaurants, dishes, or groceries, and trending topics like popular cuisines or local favorites. Highlight how you would leverage existing infrastructure and data to provide personalized results.
Ask questions to understand the scope: What entities are searchable (restaurants, dishes, stores)? What are the key metrics (latency, relevance, scale)? How are trending topics defined and updated?
Propose a system that includes data ingestion, indexing, query processing, and ranking. For trending topics, include a real-time analytics component to compute trends.
Discuss using a search engine like Elasticsearch for full-text search, with inverted indexes. Cover query understanding, ranking (e.g., learning-to-rank), and personalization.
Explain how to compute trends using stream processing (e.g., Kafka, Flink) and windowed aggregations. Store results in a low-latency store like Redis for quick retrieval.
Discuss trade-offs: consistency vs. latency, batch vs. stream processing, and cost. Address scaling with sharding, replication, and caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.