This is one of those problems that looks like a data structures question but is really about keeping your design clean under pressure.
Start by clarifying requirements and constraints, then design the data model and core operations (CRUD, priority listing, quotas, assignment with TTL, completion/expiration). Walk through the implementation using appropriate data structures, and discuss trade-offs, edge cases, and potential optimizations.
Pro tip: Emphasize how you handle concurrency and consistency, especially around TTL expiration and quota enforcement, as these are critical in a financial services context like Coinbase.
Ask questions to understand expected scale, concurrency, persistence needs, and exact semantics of priority, quotas, TTL, and expiration rules.
Define entities (Task, User, Assignment) and choose data structures (e.g., hash maps, priority queues, heaps) to support efficient operations.
Detail algorithms for CRUD, priority-based listing, quota checks, assignment with TTL, and handling completion/expiration.
Discuss race conditions, thread safety, TTL cleanup strategies, and quota enforcement under concurrent access.
Evaluate time/space complexity, scalability, and potential improvements like lazy expiration or indexing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you know whether you're ranking tasks or assignments.
Clarify the problem requirements: whether the two queries are independent or if the filtered result should be a subset of the top N. Then propose an efficient solution using a priority queue (min-heap) to find the top N tasks by priority, and for the filtered query, either filter first then heapify or use a heap with a custom comparator that ignores non-matching tasks. Discuss trade-offs between sorting, heap, and quickselect approaches.
Pro tip: Mention that if the substring filter is highly selective, filtering first can reduce the heap size and improve performance; otherwise, a single pass with a heap that only considers matching tasks avoids scanning the data twice.
Ask whether the two results are independent or if the filtered result should be a subset of the top N. Also confirm the definition of 'top' (e.g., highest priority value) and whether ties matter.
Decide between sorting the entire list (O(n log n)) or using a min-heap of size N (O(n log N)) for the top N. For the filtered query, consider filtering first or using a heap that only processes matching tasks.
For the first query: iterate through tasks, maintain a min-heap of size N, and replace the root if a task has higher priority. For the second query: either filter tasks by substring then apply the same heap logic, or modify the heap to skip non-matching tasks.
Discuss time and space complexity: O(n log N) time and O(N) space for the heap approach. Handle edge cases like N=0, N > number of tasks, empty substring, and case sensitivity.
If N is small, heap is efficient; if N is large, sorting might be simpler. For the filtered query, if the substring is very selective, filtering first can be faster. Mention that quickselect can achieve O(n) average time for top N.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The quota check was where I slipped up first.
Start by clarifying requirements and scale, then design a data model that efficiently supports TTL-based expiration and quota enforcement. Discuss trade-offs between eager vs lazy expiration and propose an API for assignment and listing with time-based queries.
Pro tip: Emphasize that TTL should be enforced at read time to avoid background job overhead, but also consider a cleanup process for storage efficiency. Mention that quota checks must be atomic to prevent race conditions in concurrent assignments.
Ask about expected read/write ratios, number of users, tasks, and whether assignments can be modified or cancelled. Confirm if TTL is strictly duration-based or can be extended.
Propose a schema for assignments with fields like userId, taskId, startTime, endTime (startTime + duration), and status. Consider indexing on userId and endTime for efficient active assignment queries.
Outline an atomic operation to check current active assignments count against quota and insert new assignment if within limit. Use transactions or conditional writes to handle concurrency.
Explain that active assignments are those where startTime <= t < endTime. For cleanup, discuss lazy deletion on read or a periodic job to remove expired assignments.
Design a query that filters by userId and time t, using an index on (userId, startTime, endTime) to quickly retrieve active assignments. Discuss pagination if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 'earliest startTime' tiebreaker for completions is the kind of thing you either get right or you don't.
Clarify the data model and requirements first, then design an efficient data structure to manage assignments and support the completion operation. Focus on the selection logic for multiple active assignments and handle edge cases like expired assignments and time-based validity.
Pro tip: Discuss trade-offs between different data structures (e.g., priority queue vs. sorted list) and mention how you would handle concurrency in a real system. Also, consider idempotency and error handling for invalid completions.
Ask questions to understand the data model: what defines an assignment (startTime, expiration, status), how tasks and users are identified, and what 'expired' means. Confirm that completing a task should mark the assignment as completed and prevent further completions.
Propose a data structure to store assignments, such as a map from (user, task) to a collection of assignments. For efficient retrieval of the earliest startTime among active assignments, consider using a min-heap or a sorted list keyed by startTime.
Outline the algorithm: given user, task, and time t, retrieve all active assignments for that pair. Filter out expired ones (e.g., where t > expiration). If multiple remain, select the one with the earliest startTime. Mark it as completed and update the data structure.
Address scenarios like no active assignments, all expired, or already completed. Define appropriate return values or exceptions. Consider concurrency: use locks or atomic operations to prevent race conditions.
Discuss time and space complexity of the operations. For example, if using a heap per (user, task), completion is O(log n) for finding and removing the earliest. Mention how this scales with many users and tasks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.