This one took most of my prep energy and I still ran out of things to say mid-way through the scaling section.
Start by clarifying requirements and scale (e.g., number of businesses, reviews, QPS, latency). Then design the core data model with entities like Business, Review, User, and Category, and discuss storage choices (SQL vs NoSQL, search index). Finally, explain how to handle read/write scaling and geospatial search using appropriate indexing and sharding strategies.
Pro tip: Emphasize trade-offs and justify your choices based on Apple's scale and privacy requirements. Mention using a hybrid approach: a relational database for transactional data and a specialized search engine (like Elasticsearch) for geospatial and full-text search.
Ask about expected number of businesses, reviews, users, read/write ratio, latency requirements, and consistency needs. This sets the stage for design decisions.
Define entities: Business (id, name, location, category, attributes), Review (id, business_id, user_id, rating, text, timestamp), User (id, name, etc.), and Category. Discuss relationships and indexing.
Select databases: e.g., MySQL/PostgreSQL for transactional data, Cassandra for write-heavy reviews, Redis for caching. Discuss sharding (by business_id or geo), replication, and read replicas.
Use geohashing, quadtrees, or R-trees to index business locations. Explain how to query nearby businesses efficiently, possibly using a search engine like Elasticsearch with geo_point.
Discuss CAP theorem implications, eventual consistency for reviews, and how to handle updates (e.g., rating aggregation). Mention caching strategies and CDN for static assets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came up as a follow-on to the Yelp question.
Start by clarifying the platform's read/write patterns and consistency requirements, then propose a layered architecture that separates read and write paths. For reads, discuss replication, caching, and CDN; for writes, cover sharding, queue-based ingestion, and asynchronous processing. Emphasize trade-offs and how you'd measure and iterate.
Pro tip: At Apple, scale discussions often hinge on user experience and privacy—highlight how your design maintains low latency and data integrity while respecting constraints like data locality and encryption.
Ask about read/write ratio, data size, latency SLAs, consistency needs, and geographic distribution. This shapes whether to prioritize caching, replication, or sharding.
Propose read replicas for horizontal scaling, multi-tier caching (client, CDN, application, database), and CDN for static/immutable content. Discuss cache invalidation and consistency trade-offs.
Introduce sharding (e.g., by user ID or geography) to distribute writes, and queue-based ingestion (e.g., Kafka) to decouple and buffer writes. Mention idempotency and backpressure handling.
Explain replication strategies (sync vs. async, multi-leader) and how to handle conflicts. Discuss eventual consistency vs. strong consistency for different data types.
Cover trade-offs like cost, complexity, and failure modes. Mention monitoring, auto-scaling, and how to evolve the design over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
By the time we got here I had maybe 15 minutes left and it showed.
Start by clarifying requirements (scale, read/write ratio, latency, consistency) and then walk through the high-level design: API endpoints, key generation, storage, redirect flow, and additional features. Emphasize trade-offs and scalability, and dive into one or two components deeply to demonstrate expertise.
Pro tip: Proactively discuss how you would handle hot keys and cache invalidation, as these are common pitfalls in URL shorteners at scale. Also, mention monitoring and alerting for system health.
Ask about scale (e.g., 100M URLs, 10K RPS), read/write ratio, latency requirements, and consistency needs. Confirm functional requirements like custom aliases, TTL, and analytics.
Outline the core components: API servers, key generation service, database, cache, and analytics pipeline. Sketch the write path (shorten) and read path (redirect).
Discuss key generation strategies (e.g., base62 encoding of auto-increment ID, hash-based, pre-generated keys) and collision handling. Choose a database (SQL vs NoSQL) and explain schema and indexing.
Explain how to scale reads with caching (e.g., Redis) and CDN, handle hot keys, and ensure high availability. Discuss rate limiting and analytics collection without impacting latency.
Detail TTL and deletion mechanisms, custom alias support, and analytics (e.g., click counts, referrers). Summarize trade-offs made and potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.