The sequential string ID generation tripped me up a bit at first.
Start by clarifying requirements and defining the data model and API contract (endpoints, request/response schemas, status codes). Then outline the implementation with a focus on clean separation of concerns, error handling, and idempotency, and finally discuss testing and potential extensions.
Pro tip: Mention idempotency for create operations and proper use of HTTP status codes (e.g., 201 Created with Location header, 404 Not Found, 400 Bad Request) to demonstrate production-level thinking.
Ask about expected scale, persistence layer, authentication, and whether the API should be RESTful. Confirm the exact fields for a task (e.g., name, priority) and any constraints.
Specify the Task entity with id, name, priority, and timestamps. Define endpoints: POST /tasks, PUT/PATCH /tasks/{id}, GET /tasks/{id}, and include request/response examples and status codes.
Describe the layers (controller, service, repository) and how you would handle validation, error cases (e.g., not found, invalid input), and concurrency. Mention idempotency for POST if needed.
Explain unit and integration tests for each operation, including edge cases like duplicate IDs, missing fields, and concurrent updates. Mention tools like JUnit, pytest, or Postman.
Briefly touch on scalability (e.g., pagination, caching), security (auth), and alternative designs (GraphQL, gRPC) to show broader system design awareness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sorting by two criteria where one is ascending and one is descending always makes me fumble the comparator for a second.
Start by clarifying the requirements: case-sensitive substring match on name, and sorting by priority descending then creation order ascending. Then outline the implementation for both functions, ensuring efficient filtering and sorting, and discuss trade-offs and edge cases.
Pro tip: Mention that you would use a stable sort to preserve creation order when priorities are equal, and consider whether the search function should also be sorted or return in original order.
Confirm the exact behavior: case-sensitive substring match on name, and sorting criteria for list (priority descending, then creation order ascending). Ask about expected input sizes and performance needs.
Decide how tasks are stored (e.g., list or array) and how to efficiently retrieve and sort them. Consider if tasks have a creation timestamp or index to determine creation order.
Iterate through tasks, check if the name contains the search string (case-sensitive), and collect matches. Discuss time complexity and potential optimizations like indexing if needed.
Sort tasks by priority descending, and for equal priorities, by creation order ascending. Use a stable sort or a custom comparator that considers both fields.
Test with empty search string, no matches, tasks with same priority, and varying creation orders. Discuss performance implications and possible improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: what defines an 'active' assignment, how quotas are enforced, and expected query patterns. Then propose a data model that supports efficient counting of active assignments at any timestamp, likely using interval trees or time-indexed storage, and discuss trade-offs between consistency and performance.
Pro tip: Mention that you would use a database with time-series or interval support (e.g., PostgreSQL with range types) and consider caching or materialized views for frequently queried timestamps to balance read performance and write overhead.
Ask about the definition of active assignments, quota limits, time window granularity, and expected query patterns (e.g., real-time checks vs. historical reporting).
Propose a schema that stores assignments with start and end timestamps, and consider indexing strategies to efficiently query active assignments at a given time.
Decide between on-the-fly counting using range queries or maintaining a running count with incremental updates, discussing trade-offs in accuracy and performance.
Describe how to enforce quotas when creating or updating assignments, including concurrency control to prevent over-assignment.
Discuss partitioning, caching, and handling time zone differences, overlapping windows, and bulk operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once the assignment data structure was in place.
Clarify the data model and definition of 'actively assigned' (e.g., assignment with start/end timestamps), then design a method that queries assignments where the current timestamp falls within the active period. Discuss indexing, timezone handling, and performance considerations for scale.
Pro tip: Mention that you would add a composite index on (user_id, start_time, end_time) to make the query efficient, and consider caching or read replicas if this is a high-traffic endpoint.
Ask about the schema: how are tasks assigned to users? Is there an assignments table with start and end timestamps? Define 'actively assigned' as start_time <= now < end_time.
Write a SQL query that selects task IDs from the assignments table where user_id matches and the current timestamp is within the active period. Use parameterized queries to avoid SQL injection.
Add an index on (user_id, start_time, end_time) to speed up lookups. Consider pagination if a user can have many tasks, and discuss caching strategies for frequently accessed data.
Ensure timestamps are stored in UTC and convert to user's timezone if needed. Handle cases where assignments have no end time (open-ended) or are soft-deleted.
Specify the method signature, return type (list of task IDs), error handling, and whether it's synchronous or asynchronous. Consider rate limiting and authentication.
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, then design a data model that supports efficient updates and queries. Explain the trade-offs between consistency and availability, and propose a solution that ensures quota slots are freed immediately upon task completion. Finally, outline how to query overdue assignments efficiently, considering indexing and time-based filtering.
Pro tip: Demonstrate awareness of concurrency issues: when a task is marked done, ensure the quota update is atomic to prevent race conditions. Also, consider using a time-series or indexed approach for overdue queries to avoid full table scans.
Ask questions to understand the scale, consistency needs, and existing system architecture. Clarify what 'immediately frees quota' means in terms of latency and consistency.
Propose a schema for tasks and quotas, including fields like task_id, assignee, status, finish_time, and quota_slot. Consider using a separate table for quotas or embedding quota info in user records.
Describe the transaction or atomic operation to mark a task as done and decrement the user's quota usage. Discuss idempotency and error handling.
Explain how to efficiently query tasks where finish_time < now and status != 'done'. Suggest indexing on finish_time and status, and possibly partitioning by time.
Address trade-offs between consistency and performance, and how the design scales with increasing users and tasks. Mention caching, read replicas, or async processing if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.