I started with the API surface and that was probably the right call since it grounded everything.
Start by clarifying requirements and scale, then design a normalized schema with a self-referencing manager_id and indexes for efficient hierarchy queries. Propose a REST API for CRUD and dedicated endpoints for reporting chain and reports, using recursive CTEs or a closure table for traversal. Discuss trade-offs, caching, and scalability.
Pro tip: Mention that reporting chains are typically shallow (5-7 levels) and change infrequently, so caching the chain per employee can drastically reduce database load. Also, consider using a closure table for O(1) ancestor/descendant queries at the cost of write complexity.
Ask about expected number of employees, read/write ratio, latency requirements, and whether the hierarchy is strictly a tree. This informs database and caching choices.
Propose an employees table with id, name, manager_id (self-referencing foreign key), and other attributes. Discuss indexing manager_id for direct reports and options for hierarchy storage (adjacency list, closure table, materialized path).
Outline RESTful endpoints: POST /employees, GET /employees/{id}, PUT /employees/{id}, DELETE /employees/{id}, GET /employees/{id}/chain, GET /employees/{id}/reports. Specify request/response formats and pagination for reports.
Explain how to retrieve the reporting chain (using recursive CTE or closure table) and direct/indirect reports (recursive CTE or closure table). Discuss performance and potential optimizations like caching.
Discuss caching strategies (e.g., Redis for chains), database sharding or read replicas, and trade-offs between adjacency list (simple writes, complex reads) and closure table (complex writes, fast reads).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: read vs write patterns, depth of hierarchy, and query types. Then compare the three models (adjacency list, closure table, path enumeration) on query performance, write cost, storage overhead, and complexity. Finally, recommend a model based on the specific use case, possibly a hybrid approach.
Pro tip: Mention that many real-world systems use a hybrid approach, such as adjacency list for writes and a closure table for reads, and that the choice depends on whether the hierarchy is static or dynamic. Also, note that Expedia's org structure might have frequent reorganizations, so write performance could be critical.
Ask about read/write ratio, depth of hierarchy, frequency of updates, and typical queries (e.g., find all reports, find manager chain).
Briefly explain adjacency list (parent_id), closure table (ancestor-descendant pairs), and path enumeration (materialized path).
Discuss query performance (reads), update cost (writes), storage overhead, and complexity for each model.
Choose a model or hybrid approach based on the requirements, and justify your choice.
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 constraints, such as the expected concurrency level and consistency needs. Then propose a layered approach: optimistic locking at the database level with versioning, combined with a conflict resolution strategy (e.g., last-write-wins or merge). Finally, discuss trade-offs and how you would handle conflicts gracefully, possibly with user intervention.
Pro tip: Mention that you would implement a 'compare-and-swap' mechanism using a version column or ETag, and that you would expose conflicts to the UI for manual resolution when automatic merging isn't safe. This shows you consider both technical and user experience aspects.
Ask about the expected frequency of concurrent updates, the tolerance for stale data, and whether the org tree is read-heavy or write-heavy. This determines the appropriate concurrency control strategy.
Propose optimistic locking (e.g., version numbers) for low contention scenarios, or pessimistic locking (e.g., row-level locks) for high contention. Explain why one is preferred over the other.
Describe how conflicts are detected (e.g., version mismatch) and resolved. Options include last-write-wins, merging changes, or rejecting the update and notifying the user.
Decide whether to handle concurrency at the database level (e.g., transactions, isolation levels), application level (e.g., distributed locks), or both. Consider scalability and performance.
Acknowledge trade-offs like performance vs. consistency, and edge cases like network partitions or long-running transactions. Mention monitoring and logging for conflict occurrences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about caching the reporting chain since it's read-heavy and changes infrequently.
Start by clarifying the hierarchical data service's access patterns and scale requirements, then systematically compare read and write tradeoffs across storage, caching, and indexing strategies. Use concrete examples like materialized paths vs. adjacency lists, and discuss how choices impact latency, throughput, and consistency.
Pro tip: Frame tradeoffs in terms of Expedia's specific use cases, such as property hierarchies or user itineraries, and mention how read-heavy workloads (e.g., search) might favor denormalization while write-heavy updates (e.g., inventory changes) need careful consistency handling.
Ask about expected read/write ratio, data size, depth of hierarchy, and consistency needs to ground the discussion in realistic constraints.
Discuss how hierarchical queries (e.g., fetching a subtree) can be optimized via denormalization, caching, or read-optimized indexes, but note increased storage and write complexity.
Explain how writes (e.g., updating a node) may require cascading updates or rebalancing, impacting latency and throughput; consider write-optimized structures like LSM trees.
Address how partitioning by hierarchy (e.g., sharding by root) affects cross-shard reads/writes, and discuss eventual vs. strong consistency tradeoffs.
Propose a hybrid strategy (e.g., caching hot subtrees, using adjacency lists for writes and materialized paths for reads) and justify based on the clarified requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.