I started with a users table and a content table, then tried to handle the multiple content types with a type column plus separate attribute tables per type.
Start by clarifying the requirements and scale, then define the core entities (User, Post, Media) and their relationships. Explain how you'd handle primary and foreign keys, including ID generation strategies and indexing for feed retrieval. Finally, discuss trade-offs like denormalization vs normalization and how to support multiple content types efficiently.
Pro tip: Mention that you'd use a globally unique ID (e.g., UUID or Snowflake) for posts to avoid collisions and enable sharding, and consider a polymorphic association for media to support different content types without schema changes.
Ask about expected read/write patterns, scale (millions of users), and latency requirements to inform design decisions.
Define User, Post, Media, and possibly Comment/Like. Establish relationships: User has many Posts, Post has many Media items.
Choose ID strategies (e.g., auto-increment, UUID, Snowflake) for each entity. Define foreign keys linking Post to User and Media to Post, ensuring referential integrity.
Use a Media table with a type field (text, image, video) and a flexible payload (e.g., JSON or separate columns). Alternatively, use inheritance or polymorphic associations.
Index foreign keys and timestamps for efficient feed queries. Discuss denormalization (e.g., caching feed) vs normalization, and how to scale with sharding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the grain and business processes the tables represent, then classify each as fact or dimension based on whether it captures measurable events or descriptive context. For each table, propose core fields that support common analytics queries, emphasizing surrogate keys, foreign keys, and time dimensions.
Pro tip: Mention that fact tables should have a clear grain and that dimension tables should be denormalized for query performance; also note that at Meta, user and event dimensions often include complex hierarchies like app, platform, and region.
Determine what each table represents: a business process (e.g., ad clicks, user sessions) or an entity (e.g., user, ad). Define the grain of fact tables (e.g., one row per click) to avoid ambiguity.
Facts contain numeric measures and foreign keys to dimensions; dimensions contain descriptive attributes. For example, an 'ad_impressions' table is a fact, while 'user' and 'ad' tables are dimensions.
Include foreign keys to related dimensions, degenerate dimensions (e.g., order_id), date/time keys, and additive measures (e.g., clicks, revenue). Ensure grain is reflected in the primary key.
Include a surrogate primary key, natural/business key, and descriptive attributes (e.g., user demographics, ad creative details). Consider slowly changing dimensions (SCDs) for historical tracking.
Check that the proposed schema supports common analytics queries (e.g., daily active users, click-through rate) and explain how joins would work.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than it should have.
Start by clarifying the requirements and constraints of sharing (e.g., permissions, visibility, scale) and then propose a data model that represents shares as first-class entities with polymorphic targets (user, group, external channel). Discuss trade-offs between embedding share data in the content model versus a separate sharing service, and outline how to handle access control, notifications, and consistency at scale.
Pro tip: Emphasize idempotency and deduplication of shares to avoid spamming recipients, and mention how you'd leverage existing infrastructure (like Meta's TAO or social graph) to avoid reinventing the wheel.
Ask questions to understand what 'sharing' means: is it one-time or persistent? What are the privacy and permission models? What scale (millions of shares per second)? This ensures you design for the right constraints.
Propose a Share entity with fields like sharer_id, target_type (user/group/channel), target_id, content_id, permissions, and timestamp. Consider using a graph model to represent relationships and enable efficient traversal.
Explain how to enforce who can see or reshare content, using role-based or attribute-based access control. Discuss how permissions propagate (e.g., if a group share is later restricted).
Discuss partitioning strategies (e.g., by user or content), caching, and asynchronous processing for notifications. Address trade-offs between strong and eventual consistency for share visibility.
Mention how to support external channels (e.g., email, third-party APIs) via adapters, and handle scenarios like revoked shares, blocked users, or expired links.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.