Start by framing the core requirements: durability, scalability, fault tolerance, and observability. Then systematically address each area, comparing trade-offs between approaches (e.g., Kafka vs DB queues, sticky vs work-stealing) and justifying choices based on Scale.ai's needs. Conclude with a cohesive architecture that integrates these components.
Pro tip: Emphasize idempotency and exactly-once semantics as the hardest part; show you understand that true exactly-once often requires transactional boundaries and deduplication, and that at-least-once with idempotent workers is often more practical.
Restate the problem: production-ready means high availability, scalability, durability, and observability. Ask about expected throughput, latency, and consistency requirements to tailor trade-offs.
Choose a durable queue (Kafka or DB) and explain how tasks are persisted, replicated, and recovered. Discuss write-ahead logs, replication factors, and fsync trade-offs.
Describe how tasks are distributed: leader election for coordination, key-based partitioning for ordering, or work-stealing for load balancing. Compare sticky workers vs work-stealing in terms of efficiency and complexity.
Detail worker failure detection (heartbeats), retries with backoff, and dead-letter queues. Discuss exactly-once vs at-least-once: exactly-once requires idempotent operations and transactional commits; at-least-once is simpler but needs deduplication.
Instrument metrics (latency, throughput, errors), set up deadline-miss alerts, and implement back-pressure via queue limits or rate limiting. Ensure graceful shutdown by draining tasks and committing offsets.
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