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The TOP_K part is where I spent most of my energy.
Start by clarifying requirements and scale, then propose a layered design: a core CRUD service backed by a database, and a dedicated ranking component (e.g., in-memory heap or sorted structure) for the TOP_K query. Discuss trade-offs between consistency, latency, and complexity, and explain how you would handle updates and deletes affecting the ranking.
Pro tip: Mention that you would maintain a secondary index or a heap for TOP_K, but also discuss how to handle frequent updates and deletes efficiently—e.g., using a lazy deletion strategy or a balanced BST—to show you think about real-world performance.
Ask about expected QPS, number of tasks, read/write ratio, and whether TOP_K needs to be real-time or can be eventually consistent. Also confirm tiebreaking rules and priority range.
Propose a database schema (e.g., tasks table with id, priority, status, etc.) and API endpoints. Discuss indexing on priority and id for efficient retrieval.
Explain how to efficiently retrieve top K tasks by priority with alphabetical tiebreaking. Consider using a min-heap of size K for static data, or a balanced BST / skip list for dynamic updates.
Describe how modifications affect the ranking structure. For example, if using a heap, updates may require re-heapification; alternatively, use a sorted set (e.g., Redis ZSET) with lazy deletion.
Compare in-memory vs. database approaches, consistency vs. performance, and how to scale horizontally (e.g., sharding by task ID or priority range). Mention caching and read replicas.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what does 'assigned at different times' mean? Is it sequential or overlapping? Then propose a data model that separates the task from its assignments, using a join entity with time bounds. Discuss trade-offs between normalization and query performance, and how to handle concurrency and history.
Pro tip: Mention that you would add database constraints (e.g., exclusion constraints for overlapping time ranges) to enforce business rules at the data layer, not just in application code. This shows you think about data integrity and concurrency.
Ask questions to understand if assignments can overlap, if historical assignments need to be preserved, and if there are constraints like a maximum number of concurrent assignees.
Identify Task and User as core entities, and introduce an Assignment entity to represent the many-to-many relationship with temporal attributes.
Specify fields such as task_id, user_id, start_time, end_time (nullable for ongoing), and possibly role or status. Consider using a composite key or a surrogate key.
Explain how to query current assignees (end_time is null or > now), and how to enforce non-overlapping assignments per user or per task using database constraints or application logic.
Compare normalized vs. denormalized approaches, indexing strategies for time-range queries, and how to handle high write throughput or large history.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through indexing with two dicts: one keyed by task_id, one by assignee, both holding references to the same Assignment objects.
Start by clarifying the requirements and scale, then propose a data model that supports efficient lookups in both directions (task-to-user and user-to-task). Discuss how to implement each operation using appropriate data stores and indexing strategies, and consider trade-offs between consistency, latency, and scalability.
Pro tip: Mention the need for idempotency and conflict resolution (e.g., using versioning or timestamps) to handle concurrent assignments, and highlight how you would monitor and alert on assignment failures or inconsistencies.
Ask about expected read/write patterns, data volume, latency requirements, and consistency needs. This informs the choice of data store and indexing strategy.
Propose a schema that supports both task-centric and user-centric queries, such as a assignments table with indexes on task_id and user_id, or a denormalized approach with separate indexes.
For ASSIGN and UNASSIGN, use transactions or conditional writes to ensure atomicity. For GET_ASSIGNMENTS_FOR_TASK and GET_ASSIGNMENTS_FOR_USER, leverage indexes or materialized views to achieve low-latency reads.
Discuss sharding, caching, and replication strategies to handle scale. Consider trade-offs between strong and eventual consistency, and how to handle concurrent assignments.
Compare SQL vs NoSQL, normalized vs denormalized, and synchronous vs asynchronous processing. Explain why your chosen approach fits the requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.