Start by clarifying requirements: job types (one-time, immediate, recurring), scale, latency, and execution guarantees. Then design a high-level architecture with a job store, scheduler, message queue, and worker fleet, and dive into fault tolerance, retries, and exactly-once semantics using idempotency and deduplication.
Pro tip: Explicitly discuss the trade-off between at-least-once and exactly-once delivery, and propose a practical solution like idempotent workers with a deduplication cache to achieve effectively-once semantics. This shows you understand real-world constraints and can balance correctness with performance.
Ask about job types, expected scale (jobs per second, worker count), latency requirements, and execution guarantees (at-least-once vs exactly-once). Also clarify if jobs can be canceled or updated.
Propose a distributed system with a persistent job store (e.g., database or distributed log), a scheduler service that triggers jobs, a message queue (e.g., Kafka) for decoupling, and a pool of workers that execute jobs.
Explain how one-time and immediate jobs are enqueued, and how recurring jobs are handled using a timing wheel or cron-like scheduler that periodically scans for due jobs and pushes them to the queue.
Describe mechanisms for worker failures: job leases with timeouts, retries with exponential backoff, dead-letter queues for poison messages, and ensuring the scheduler itself is highly available (e.g., leader election).
Discuss how to achieve at-least-once delivery with idempotent job execution, and for exactly-once, use deduplication (e.g., unique job IDs with a dedup store) and transactional writes to track job state.
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