This is the main question and it ate the whole session.
Start by clarifying requirements and scale, then design a decoupled architecture with an API layer, a durable job queue, and a pool of GPU workers. Focus on the asynchronous job lifecycle, status tracking, and reliable notifications, and discuss trade-offs around scalability, cost, and latency.
Pro tip: Emphasize idempotency and failure handling: AI generation is expensive and long-running, so design for retries, dead-letter queues, and exactly-once notifications to avoid duplicate charges or user confusion.
Ask about expected QPS, video length, GPU types, latency SLOs, and notification channels. Establish assumptions for daily active users and peak load.
Propose a microservices architecture: API gateway, job service, message queue (e.g., Kafka/SQS), worker pool with GPU instances, status database, and notification service.
Define job states (queued, processing, completed, failed) and design a schema for jobs, status history, and user notifications. Use a database like PostgreSQL or DynamoDB for durability.
Discuss horizontal scaling of workers, auto-scaling based on queue depth, partitioning, retries with exponential backoff, and dead-letter queues for failed jobs.
Design a notification service that consumes job completion events and sends via email, webhook, or push. For status tracking, provide an API endpoint that queries the job database and consider caching for frequent polls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Favorite follow-up of the whole interview because I actually had a decent answer.
Start by acknowledging that webhooks are unreliable and should be treated as an optimization, not the source of truth. Then propose a reconciliation loop that polls the generation backend for job status, combined with idempotent state transitions and a durable job store. Finally, discuss trade-offs like polling frequency, cost, and latency, and how to handle duplicates via idempotency keys.
Pro tip: Emphasize that the webhook should only trigger an immediate status check, not directly update the job state; this decouples the unreliable event from the critical state transition. Also mention that you'd use exponential backoff with jitter for polling to balance latency and load.
Explain that webhooks can be lost or duplicated, so they should only signal that a status check is needed, not directly mutate job state. The system must have a fallback mechanism to detect completion independently.
Design a periodic poller that queries the generation backend for the status of all non-terminal jobs. This ensures eventual consistency even if webhooks fail. Use exponential backoff with jitter to avoid thundering herds.
Use idempotency keys or conditional updates (e.g., compare-and-swap) so that duplicate webhooks or poll results don't cause double-processing. The job state machine should only allow valid transitions to terminal states.
Store job status in a durable database with a state machine (e.g., pending, running, succeeded, failed). This allows the reconciliation loop to query non-terminal jobs and ensures state survives restarts.
Address latency vs. cost: polling more frequently reduces latency but increases load. Suggest adaptive polling based on job age or expected duration, and using webhooks to trigger immediate checks to reduce latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the sudden capacity drop and the need for immediate load shedding. Then, outline a tiered priority system that ensures paid users are served first, while implementing fairness mechanisms like weighted fair queuing and admission control to prevent queue explosion. Finally, discuss dynamic scaling and graceful degradation to maintain system stability.
Pro tip: Emphasize the importance of monitoring and adaptive control: a static priority scheme can lead to starvation, so incorporate feedback loops that adjust based on queue length and latency. Also, mention the trade-off between fairness and throughput—sometimes it's better to reject low-priority requests early to keep the system responsive.
Immediately detect the capacity drop and classify incoming requests by priority (e.g., paid vs. free, interactive vs. batch).
Implement strict priority scheduling for paid users, ensuring their requests are processed first, possibly with reserved capacity.
Use weighted fair queuing among remaining users and set admission thresholds to reject or defer low-priority requests when queues exceed limits.
Introduce backpressure, rate limiting, and load shedding to keep queue lengths bounded, and consider dropping or redirecting excess traffic.
Continuously monitor system metrics and adjust policies dynamically to balance fairness, latency, and throughput as conditions change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went straight to client-generated idempotency keys on the submit endpoint, checked against a dedup table before enqueuing.
Start by clarifying the system architecture: an API gateway receives the request, then a job orchestrator checks idempotency before enqueuing a video generation job. Walk through the request lifecycle, emphasizing where the idempotency key is extracted, stored, and validated, and how you handle concurrent duplicate requests.
Pro tip: Mention that idempotency keys should be scoped to the user and operation, and that you should return the same response for duplicates, not just avoid duplicate work. Also, discuss how you handle failures after the idempotency record is written but before the job completes.
Explain that the client generates a unique idempotency key (e.g., UUID) per logical request and sends it in a header like 'Idempotency-Key'. If the client doesn't provide one, the server can derive a key from the user ID and a hash of the prompt, but that's less reliable.
The API gateway (or a dedicated idempotency service) checks a fast data store (e.g., Redis) for the key. If the key exists and the request is still processing, return a 409 Conflict or a 202 Accepted with a status URL; if completed, return the cached response.
If the key is new, atomically write it to the store with a 'processing' status and enqueue the video generation job. Use a transaction or a Lua script in Redis to avoid race conditions between concurrent duplicate requests.
When the job finishes, update the idempotency record with the result (e.g., video URL) and set a TTL. Subsequent requests with the same key return the cached result instead of re-generating.
If the job fails, either delete the idempotency key to allow retries or store the failure and return an error. Discuss trade-offs: allowing retries vs. preventing duplicate work on transient failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The double-notification constraint is what makes this tricky and I nearly forgot about it.
Start by outlining a detection strategy using metrics and tracing to identify stuck jobs, then systematically diagnose root causes by examining job state, dependencies, and resource contention. Finally, describe a recovery process that ensures idempotency and exactly-once semantics to avoid double-charging or double-notifying.
Pro tip: Emphasize idempotency keys and transactional outbox patterns to guarantee exactly-once side effects, and mention the importance of a dead-letter queue for manual inspection and safe retries.
Set up monitoring and alerting on job duration metrics, comparing against p99 generation time, and use distributed tracing to identify stuck jobs.
Investigate root causes by checking job state, dependencies, resource utilization, and logs for errors or deadlocks.
Design a recovery plan that includes safe cancellation or retry mechanisms, ensuring idempotency to prevent duplicate side effects.
Execute recovery steps carefully, using idempotency keys and transactional boundaries to avoid double-charging or double-notifying.
Conduct a post-mortem to identify improvements, such as better timeouts, circuit breakers, and enhanced monitoring to prevent recurrence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.