Start by clarifying requirements and constraints, then propose a full-stack architecture with a RESTful API, a relational database schema, and a frontend that persists sort preferences via localStorage or URL parameters. Walk through each CRUD operation, discuss sorting implementation on both backend and frontend, and highlight trade-offs such as pagination, indexing, and optimistic UI updates.
Pro tip: Emphasize that sort preference persistence should be handled client-side (e.g., localStorage or URL query params) to avoid unnecessary server round-trips and keep the API stateless. Also, mention that sorting by user ID may require a composite index or denormalization for performance.
Ask about expected scale, authentication, comment threading, and whether sorting should be server-side or client-side. Confirm that sort preference persistence is per-user and per-browser.
Propose a comments table with fields like id, user_id, content, created_at, updated_at. Define REST endpoints: POST /comments, GET /comments?sort=field&order=asc|desc, PUT /comments/:id, DELETE /comments/:id.
For sorting, either handle it in the database with ORDER BY and indexes, or fetch all and sort client-side for small datasets. Persist sort preference in localStorage or URL query parameters so it survives refresh.
Discuss pagination vs. infinite scroll, optimistic UI updates for responsiveness, error handling, and security (e.g., authorization for update/delete). Mention indexing strategies for sorting by timestamp or user ID.
Recap the architecture, highlight how each requirement is met, and invite feedback or questions to ensure alignment with the interviewer's expectations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose an asynchronous architecture with a job queue and worker pool. Explain how to handle request acceptance, background processing, status tracking, result retrieval, and UI ordering, emphasizing trade-offs and scalability.
Pro tip: Discuss idempotency and exactly-once processing to handle retries and duplicates, and mention how to design for failure recovery and monitoring. This shows maturity beyond basic functionality.
Ask about expected load, latency SLAs, image types, user expectations, and consistency requirements. This ensures the design meets actual needs.
Propose an API gateway for request acceptance, a message queue (e.g., RabbitMQ, Kafka) for job distribution, and a pool of workers for image generation. Mention storage for results (e.g., S3) and a database for job metadata.
Describe how the API validates requests, creates a job record with a unique ID, enqueues the job, and immediately returns a 202 Accepted with the job ID and status URL.
Explain how workers pick up jobs, update status (e.g., pending, processing, completed, failed), and store results. Include error handling, retries, and dead-letter queues.
Detail how clients poll or subscribe (e.g., WebSockets, SSE) for status updates and fetch results. For UI ordering, discuss using a monotonic sequence number or timestamp to sort items consistently, even with out-of-order completions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.