Started with POST /processFiles taking a JSON array of file IDs, returns per-file results with status and any output or error.
Start by clarifying requirements (file count limits, file sizes, timeout, partial failure handling) and then propose a synchronous REST endpoint that accepts a list of file IDs and returns a single response with per-file results. Outline the request/response schema, then describe the backend components (API gateway, validation, parallel processing, aggregation) and discuss trade-offs like timeouts and partial failures.
Pro tip: Acknowledge the synchronous constraint but mention that for large lists you'd need pagination or async patterns; showing awareness of limits demonstrates maturity. Also, define a clear error contract per file so clients can handle partial failures gracefully.
Ask about expected number of file IDs, file sizes, processing time, timeout limits, and whether partial success is acceptable. This shapes the design and shows you think before coding.
Propose a POST endpoint like /process-files that accepts a JSON body with an array of file IDs. Define a response with an array of results, each containing file ID, status, and either data or error.
Describe the API layer (validation, auth), a processing orchestrator that fans out requests to a file service or worker pool, and an aggregator that collects results. Mention parallel processing with bounded concurrency.
Explain how to handle individual file failures without failing the whole request, using per-file status codes and error messages. Discuss timeouts and how to avoid blocking indefinitely.
Highlight limitations of synchronous processing for large lists and suggest alternatives like async jobs or pagination. Mention idempotency, rate limiting, and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then design an asynchronous job processing system with a queue, worker pool, and persistent job store. Focus on idempotency, status tracking, and crash recovery, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Use idempotency keys and a state machine for job status to handle retries and crashes gracefully; this shows you understand real-world reliability concerns beyond just happy-path design.
Ask about expected file sizes, number of files, processing time, concurrency, and SLA for job completion. Determine if results need to be stored and for how long.
Propose a system with an API layer for job submission and status, a message queue (e.g., SQS, Kafka) for decoupling, and a pool of workers for processing. Include a database for job metadata and a blob store for results.
Define job states (e.g., PENDING, PROCESSING, COMPLETED, FAILED) and transitions. Use idempotency keys to ensure duplicate submissions don't create multiple jobs. Workers should check job status before processing to avoid duplicate work.
Ensure jobs are persisted before enqueueing. Use visibility timeouts or acknowledgments so failed workers don't lose jobs. Implement retries with exponential backoff and dead-letter queues for poison messages.
Provide APIs for clients to poll job status and retrieve results. Store results in a durable store (e.g., S3) and return pre-signed URLs. Consider push notifications (webhooks) for completion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.