I started with the obvious stuff: a queue for immediate jobs, a separate store for delayed ones with a polling loop to promote them when their time comes.
Start by clarifying requirements (scale, latency, durability, ordering) and then propose a high-level architecture that separates immediate and delayed job handling. Use a time-ordered data structure (e.g., min-heap or timing wheel) for delayed jobs and a queue for immediate jobs, with workers polling for due jobs. Discuss trade-offs between polling and event-driven approaches, and how to ensure reliability and scalability.
Pro tip: Emphasize the importance of idempotency and exactly-once semantics, as job schedulers often face duplicate execution risks. Also, mention how you would handle clock skew and time zone issues, which are common pitfalls in distributed scheduling.
Ask about scale (jobs per second), latency requirements, durability, ordering guarantees, and whether jobs can be cancelled or updated. This shapes the design and trade-offs.
Propose components: a job submission API, a persistent job store (e.g., database), a scheduler service, and worker pool. Separate immediate and delayed job paths for efficiency.
Use a time-ordered data structure like a min-heap or hierarchical timing wheel to efficiently find due jobs. Discuss how to persist and recover this structure.
Workers poll for due jobs, execute them, and update status. Ensure at-least-once execution with idempotency, and handle failures with retries and dead-letter queues.
Discuss partitioning (e.g., by job ID or time), using distributed queues (e.g., Kafka, SQS), and trade-offs between polling frequency, latency, and resource usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The exactly-once question is kind of a trap in the best way.
Start by clarifying the system's requirements and constraints, then explain the failure modes and how your design handles each (worker crashes, scheduler failures, retries, duplicates). Finally, discuss the trade-offs and whether exactly-once is achievable, likely concluding that it's not truly possible but can be effectively simulated with idempotency and deduplication.
Pro tip: Acknowledge that exactly-once is a myth in distributed systems; instead, focus on achieving effectively-once semantics through idempotent operations and deduplication, which shows deep understanding and pragmatism.
Ask about the system's consistency, availability, and latency requirements, and whether the interviewer expects a theoretical or practical answer. This sets the stage for a tailored response.
Describe how your design detects and recovers from worker crashes (e.g., heartbeats, leases) and scheduler failures (e.g., leader election, replication). Mention retry policies with exponential backoff and jitter.
Discuss how retries can cause duplicates and how you mitigate them using idempotent operations, unique keys, or deduplication stores. Emphasize that exactly-once delivery is impossible, but effectively-once processing can be achieved.
Compare at-least-once vs. at-most-once vs. effectively-once, and explain the trade-offs in terms of complexity, performance, and cost. Mention how Figma's specific use case might influence the choice.
Summarize that exactly-once execution is not achievable in distributed systems due to the Two Generals' Problem, but you can design for effectively-once semantics with idempotency and deduplication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.