Start by clarifying requirements and scale, then propose a clean data model with a unique ID, name, and priority. Outline the API operations (create, get, update) and discuss storage choices, indexing, and concurrency handling. Walk through the design with a focus on correctness, performance, and extensibility.
Pro tip: Demonstrate awareness of real-world concerns like idempotency, race conditions, and priority updates affecting ordering. Mention how you'd handle scaling and monitoring, showing you think beyond basic CRUD.
Ask about expected scale, consistency needs, and whether tasks need ordering by priority. Confirm if updates can change priority and how conflicts should be resolved.
Define a Task entity with id (UUID or auto-increment), name (string), and priority (integer or enum). Discuss indexing on priority for efficient retrieval.
Specify create (POST /tasks), get (GET /tasks/{id}), and update (PUT/PATCH /tasks/{id}). Include request/response schemas and status codes.
Select a database (SQL for ACID, NoSQL for scale) and discuss optimistic locking or versioning to handle concurrent updates.
Address caching, sharding, and replication. Compare SQL vs NoSQL and explain how priority updates affect ordering and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: define priority ordering, tie-breaking, and whether the substring match is case-sensitive. Then propose a data structure that supports efficient top-N queries, such as a balanced BST or heap, and for substring filtering, consider a trie or inverted index. Discuss trade-offs between precomputation and on-the-fly filtering, and outline API design for both queries.
Pro tip: Mention that for substring queries, a naive scan is O(M) per query, but an index like a suffix tree or n-gram index can reduce it; however, consider update frequency and memory overhead. Also, suggest using a priority queue with a size limit for top-N to avoid sorting the entire set.
Ask about priority definition, tie-breaking rules, substring matching semantics (case sensitivity, partial words), and expected query frequency vs. update frequency.
For top-N overall, use a balanced BST or a max-heap with a size cap. For substring filtering, consider a trie, suffix tree, or inverted index on task names.
For top-N overall, traverse the BST in reverse order or pop from heap. For substring, first retrieve matching tasks via index, then apply top-N selection on that subset.
Compare time/space complexity of different approaches, and discuss whether to maintain separate indexes or combine them, considering update costs and memory.
Specify method signatures, e.g., getTopNTasks(int n) and getTopNTasksByNameSubstring(int n, String substring). Mention pagination or streaming for large N.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The quota constraint tripped me up more than I expected.
Start by clarifying requirements and scale, then design a data model with User, Task, and Assignment entities, enforcing quotas and TTLs. Discuss trade-offs between SQL and NoSQL, and how to efficiently query active assignments.
Pro tip: Mention using a composite index on (user_id, expires_at) and a partial index for active assignments to ensure fast lookups and efficient cleanup. Also, consider using a TTL index in MongoDB or a scheduled job for expiration.
Ask about expected read/write patterns, scale (users, tasks, assignments), and whether TTL is strict or eventual. Confirm if quota is per user and if tasks can be assigned to multiple users simultaneously.
Propose tables/collections for User (with quota), Task, and Assignment (with user_id, task_id, expires_at). Discuss primary keys, foreign keys, and indexes.
Explain how to enforce max active tasks per user (e.g., count active assignments before insert) and how TTL is implemented (e.g., expires_at field, background job, or database TTL feature).
Describe how to list a user's active assignments efficiently, using an index on (user_id, expires_at) and filtering by expires_at > now.
Compare SQL vs NoSQL, discuss consistency vs availability, and how to handle high write throughput and cleanup of expired assignments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and requirements, then design data structures that support efficient completion and expiration queries. Discuss trade-offs between different approaches (e.g., scanning vs. indexing) and outline algorithms for completing the earliest-start assignment and listing expired assignments. Finally, analyze time and space complexity and consider edge cases.
Pro tip: Demonstrate awareness of real-world constraints by discussing how to handle concurrent updates and how to efficiently query expired assignments without full scans, perhaps using a time-ordered index or bucketing by TTL.
Ask questions to understand the task, assignment, and user entities, their relationships, and the exact semantics of TTL and expiration. Confirm whether timestamps are inclusive/exclusive and how to handle ties.
Propose data structures to store tasks, assignments, and user-assignment mappings. Consider how to efficiently find the earliest-start assignment for a task and how to list expired assignments for a user.
Outline the algorithm: given a task and timestamp, filter out expired assignments, then select the one with the earliest start time. Discuss how to update state (e.g., mark assignment as completed).
Describe how to retrieve all assignments for a user that are expired as of the given time. Consider whether to maintain a separate index or compute on the fly.
Discuss time and space complexity of the proposed solution, and compare with alternatives (e.g., scanning vs. indexing). Mention potential optimizations and scalability considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.