I went straight to the feed generation question because I knew that was the meat of it.
Start by clarifying requirements and scale (e.g., 100M daily users, 500M tweets/day, read-heavy 100:1 ratio). Then design a high-level architecture with separate write and read paths, using a fan-out-on-write approach for the home feed, and dive into data models, storage choices, and trade-offs. Finally, discuss bottlenecks and scaling strategies like caching, sharding, and async processing.
Pro tip: Explicitly call out the fan-out trade-off: fan-out-on-write gives fast reads but expensive writes for celebrities; propose a hybrid approach where high-follower accounts use fan-out-on-read. This shows you understand real-world complexity beyond textbook solutions.
Ask about expected user base, read/write ratio, latency requirements, and consistency needs. Establish functional requirements (post, follow, home feed) and non-functional (high availability, eventual consistency).
Sketch components: API gateway, write service, read service, fan-out service, storage (tweet store, graph store, feed cache), and message queue. Explain data flow for posting and feed retrieval.
Design schemas for tweets, follows, and feeds. Choose databases: e.g., Cassandra for tweets (time-series), Redis for feed cache, and a graph DB or relational for follows. Discuss sharding and replication.
Compare fan-out-on-write vs. fan-out-on-read. Propose a hybrid: push tweets to followers' feed caches for normal users, pull for celebrities. Explain how to merge and rank tweets.
Address bottlenecks: hot keys, celebrity problem, cache invalidation, and consistency. Discuss techniques like sharding, async processing, and caching. Summarize trade-offs made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Walked through a post endpoint, a follow endpoint, and a feed fetch endpoint.
Start by clarifying the system's core purpose and primary use cases, then define the key resources and their relationships before designing RESTful APIs with standard CRUD operations and appropriate data models. Emphasize consistency, scalability, and alignment with Asana's domain (e.g., tasks, projects, users) while discussing trade-offs.
Pro tip: Show awareness of Asana's existing API patterns and data model conventions (e.g., tasks belong to projects, projects belong to teams) to demonstrate domain knowledge and practical experience. Also, mention how you would handle permissions and data consistency across related resources.
Ask clarifying questions to understand the system's purpose, key entities, and expected operations. Identify primary use cases and constraints (e.g., scale, real-time updates).
List the main entities (e.g., tasks, projects, users) and define their relationships (one-to-many, many-to-many). Consider how they map to Asana's domain.
For each resource, define fields, data types, and constraints. Include IDs, timestamps, and references to related resources. Discuss normalization vs. denormalization trade-offs.
Specify RESTful endpoints (e.g., GET /tasks, POST /projects) with standard CRUD operations. Include query parameters for filtering, pagination, and sorting. Consider batch operations and webhooks.
Discuss authentication, authorization, rate limiting, error handling, versioning, and data consistency. Mention how the API supports integrations and extensibility.
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 requirements of the feed (e.g., number of users, read/write ratio, latency SLAs). Then propose a storage solution that balances consistency, availability, and partition tolerance, and layer caching strategies to optimize read performance and reduce database load. Discuss trade-offs between different storage and caching options, and how they align with Asana's needs.
Pro tip: Emphasize the importance of measuring and monitoring cache hit rates and storage performance in production, and be prepared to discuss how you would iterate on the design based on real-world metrics.
Ask questions to understand the scale (e.g., daily active users, feed size), read/write patterns, latency requirements, and consistency needs. This ensures your design is tailored to the actual problem.
Evaluate storage options (SQL vs NoSQL, distributed databases like Cassandra, DynamoDB, or PostgreSQL with sharding) based on scalability, consistency, and query patterns. Justify your choice with trade-offs.
Propose a multi-layer caching approach (e.g., CDN, application-level cache like Redis/Memcached, and database caching). Discuss cache invalidation, eviction policies, and consistency models.
Explain how the system scales horizontally (sharding, replication) and handles failures (redundancy, failover). Mention monitoring and auto-scaling.
Summarize key trade-offs (e.g., consistency vs latency, cost vs performance) and how you would validate and iterate on the design using metrics and A/B testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the platform's scale, traffic patterns, and abuse vectors, then propose a layered defense combining rate limiting, abuse detection, and observability. Emphasize trade-offs between user experience, security, and system complexity, and tie your choices to Asana's collaborative work management context.
Pro tip: Show maturity by discussing how you'd handle false positives in abuse detection and how you'd iterate on thresholds using observability data, rather than proposing a static solution.
Ask about scale (users, requests per second), types of abuse (spam, scraping, DDoS), and existing infrastructure. This ensures your solution is tailored and not over-engineered.
Propose a multi-tiered approach: global, per-user, and per-endpoint limits using algorithms like token bucket or sliding window. Discuss where to enforce (API gateway, service mesh, application) and how to handle distributed state (e.g., Redis).
Outline anomaly detection using heuristics (e.g., request patterns, IP reputation) and machine learning models. Explain how to integrate with rate limiting (e.g., dynamic throttling) and handle false positives with manual review or challenge mechanisms.
Define key metrics (request rates, error rates, latency, abuse flags), logging (structured logs with context), and tracing (distributed tracing for request flows). Describe dashboards and alerts for real-time monitoring and post-incident analysis.
Acknowledge trade-offs: strict limits may hurt legitimate users, detection may have false positives, observability adds overhead. Explain how you'd use A/B testing, canary releases, and feedback loops to refine thresholds and algorithms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.