This is the kind of question that looks straightforward until you realize how many edge cases the schema has to handle.
Start by clarifying the core entities and relationships, then model the share action as a polymorphic relationship between content and destination. Discuss trade-offs between normalization and denormalization, and consider scalability and query patterns.
Pro tip: Mention that shares are often immutable and can be modeled as events, which simplifies auditing and allows for eventual consistency. Also, consider using a graph model to represent relationships between users, content, and destinations.
List the main entities: User, Content (with subtypes Text, Image, Video), Destination (Thread, Feed, DirectMessage), and Share. Define their attributes and relationships.
Decide how to represent a share: as a separate entity linking content, user, destination, and metadata (timestamp, privacy). Consider polymorphism for content and destination types.
Evaluate SQL vs NoSQL, normalization vs denormalization. Discuss how to handle different content types and destinations efficiently, possibly using separate tables/collections per type.
Consider read/write patterns: feeds require fast reads, threads need chronological ordering, DMs need privacy. Discuss indexing, sharding, and caching strategies.
Highlight trade-offs like consistency vs availability, and how to extend the model for features like resharing, analytics, or deletion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked a little on partitioning strategy.
Start by clarifying the query patterns and scale requirements, then propose a composite index on (share_id, timestamp) and foreign keys with appropriate referential actions. For partitioning, recommend range partitioning on timestamp (e.g., monthly) with sub-partitioning by share_id hash if needed, and discuss trade-offs like partition pruning and maintenance overhead.
Pro tip: Emphasize that partitioning and indexing decisions must be driven by the actual query patterns and data volume; mention that over-indexing can hurt write performance and that foreign keys add overhead but ensure integrity—so balance is key.
Ask about query patterns (e.g., time-range, per-share aggregations), data volume, read/write ratio, and latency SLA to tailor your answer.
Propose a composite index on (share_id, timestamp) for per-share time-series queries, and consider covering indexes for common aggregations. Mention that foreign keys should be indexed on the referencing side.
Add foreign keys from shares to users and posts (or other entities) with ON DELETE CASCADE or RESTRICT based on business rules, and ensure indexes on those FK columns.
Recommend range partitioning on timestamp (e.g., monthly) for efficient time-range queries and easy data retention. If share_id is a common filter, consider sub-partitioning by hash on share_id.
Address trade-offs: partition pruning benefits vs. cross-partition queries, index maintenance cost, and how to handle hot partitions. Mention that sharding may be needed beyond a single database.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and the definition of share rate, then outline the SQL query using aggregation and division. Walk through the query step-by-step, explaining how to handle potential issues like zero impressions and data grouping.
Pro tip: Mention the importance of using NULLIF or CASE to avoid division by zero, and consider whether to filter out rows with zero impressions before aggregation to improve performance.
Confirm the table structure (e.g., events table with content_id, event_type, user_id) and define share rate as shares divided by impressions per content item.
Use conditional aggregation (e.g., SUM(CASE WHEN event_type = 'share' THEN 1 ELSE 0 END)) to count shares and impressions for each content_id.
Divide shares by impressions, using NULLIF or CASE to handle zero impressions, and optionally round the result.
Combine the aggregation and division into a single query, grouping by content_id and ordering or filtering as needed.
Address handling of zero impressions, data freshness, and potential performance improvements like filtering before aggregation.
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 I expected.
Start by clarifying the definition of a share-driven engagement and the attribution window, then outline a measurement framework that combines deterministic tracking (e.g., share IDs, referrer parameters) with probabilistic methods (e.g., holdout experiments) to isolate the causal impact of shares. Emphasize the trade-offs between accuracy, scalability, and privacy, and propose a validation plan using A/B tests.
Pro tip: Acknowledge that perfect attribution is impossible due to cross-device and privacy constraints, and propose a pragmatic hybrid approach that balances precision with scalability. Show awareness of Meta's specific challenges like iOS ATT and the need for aggregated measurement.
Clarify what constitutes a share-driven engagement (e.g., a view or click on a shared link) and specify the attribution window (e.g., 24 hours, 7 days) based on product context and user behavior.
Decide between deterministic methods (unique share IDs, referrer URLs, UTM parameters) and probabilistic methods (device fingerprinting, IP matching), considering platform constraints and privacy regulations.
Use holdout experiments (e.g., randomize users to see shares or not) or synthetic control to measure incremental engagement attributable to shares, isolating the causal effect from organic traffic.
Run A/B tests to compare deterministic attribution against holdout results, calibrate for undercounting (e.g., cross-device), and adjust for biases like self-selection.
Set up dashboards to track share-driven engagement over time, monitor for data quality issues, and iterate on the methodology as privacy landscapes and user behaviors evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the most interesting part of the whole interview.
Start by clarifying the schema and defining what constitutes a 'share' and a 'new viewer' event, then propose a graph-based model where nodes are users and edges are share events. Compute the viral coefficient as the average number of new viewers generated per share, and extend it to multiple hops by summing the expected new viewers at each depth using a geometric series or by traversing the share graph up to a depth limit.
Pro tip: Mention that in practice, you'd cap the number of hops (e.g., 3-5) because the coefficient decays and tracking beyond that is noisy; also highlight the importance of deduplicating viewers across hops to avoid double-counting.
Ask about the tables/columns available (e.g., shares, views, user IDs, timestamps) and define what counts as a 'share' and a 'new viewer' (e.g., first-time viewer of the content).
Represent users as nodes and share events as directed edges from sharer to viewer, possibly with timestamps to track chains.
For each share, count the number of new viewers it generates; average this over all shares to get the base coefficient (k).
Use the graph to traverse from original sharer to viewers, then to viewers of those viewers, etc., summing new viewers at each depth. Alternatively, if k is stable, compute total new viewers as k + k^2 + k^3 + ... up to a depth limit.
Ensure each viewer is counted only once across all hops, and consider time windows, decay, and computational limits (e.g., using BFS with visited set).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the share data model and how privacy settings (e.g., public, friends, only me) and audience scoping (e.g., custom lists) are represented as attributes. Then explain how these attributes affect data collection, aggregation, and metric computation, emphasizing trade-offs between accuracy, privacy, and performance. Conclude with how you would design the system to handle these constraints at scale.
Pro tip: Demonstrate awareness that privacy settings are not just metadata but affect data visibility and aggregation logic, and that metrics must respect user privacy while still providing meaningful insights. Mention techniques like differential privacy or k-anonymity to show depth.
Describe the entities and relationships: shares, users, privacy settings, and audience scopes. Explain how privacy settings and audience scoping are stored (e.g., as enums, lists, or bitmasks) and how they influence data access.
List key metrics (e.g., share count, reach, engagement) and explain how privacy settings affect their computation. For example, private shares may not be counted in public metrics, or audience scoping may limit reach.
Discuss trade-offs between data completeness, privacy compliance, and system performance. For instance, aggregating private shares may require anonymization, which can reduce accuracy.
Suggest how to model and compute metrics while respecting privacy, such as using separate tables for private/public data, applying aggregation with privacy-preserving techniques, or using access control layers.
Explain how the design scales with increasing privacy options and audience scopes, and how to handle changes in privacy policies over time without breaking metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.