Start by clarifying requirements and scale, then propose a high-level architecture using a priority queue (e.g., min-heap) for delayed jobs and an immediate queue for instant jobs. Discuss trade-offs between in-memory vs. persistent storage, polling vs. event-driven execution, and how to handle failures and scalability.
Pro tip: Emphasize idempotency and at-least-once execution semantics, and mention how you would monitor job lag and queue depth to detect issues early.
Ask about expected job volume, latency requirements, durability needs, and whether jobs can be cancelled or updated. This shapes the design significantly.
Propose a scheduler service with two queues: an immediate FIFO queue and a delayed priority queue (min-heap by execution time). Workers pull from both, with a timing mechanism to move due delayed jobs to the immediate queue.
Decide between in-memory (fast but volatile) and persistent storage (e.g., database, Redis sorted set). Discuss trade-offs and how to recover from failures.
Explain how workers execute jobs, handle retries, and scale horizontally. Address how to avoid duplicate execution and ensure at-least-once semantics.
Discuss trade-offs like polling vs. event-driven, precision of delays, and handling of long delays (e.g., 1 hour). Mention monitoring and alerting.
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This is where I had to think on my feet a bit.
Start by clarifying that exactly-once execution is typically achieved through at-least-once delivery combined with idempotent processing and deduplication. Then discuss the trade-offs between different approaches, such as distributed transactions, consensus protocols, and idempotency keys, and how they apply to a distributed scheduler like Figma's.
Pro tip: Emphasize that true exactly-once is impossible in distributed systems without assumptions; instead, focus on making operations idempotent and using deduplication to achieve effectively-once semantics. This shows you understand the theoretical limits and practical solutions.
Clarify what 'exactly-once' means in the context of job scheduling: no duplicate executions and no missed executions, even under failures. Discuss the CAP theorem and why strict exactly-once is impossible without coordination.
Explain that at-least-once delivery with idempotent jobs is a common practical approach. Alternatively, discuss at-most-once with acknowledgment, but note the risk of job loss.
Describe how to make job execution idempotent (e.g., using unique job IDs, idempotency keys, or state checks) and how to deduplicate using a distributed store like Redis or a database with unique constraints.
Discuss using distributed locks, leader election, or consensus protocols (e.g., Raft, Paxos) to ensure only one scheduler instance triggers a job. Also cover retries with exponential backoff and dead-letter queues.
Mention the importance of monitoring for duplicate executions and missed jobs, and using auditing and reconciliation to detect and correct issues.
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Start by clarifying the job execution model (e.g., batch, streaming, distributed) and the failure semantics (at-least-once, at-most-once, exactly-once). Then walk through the failure lifecycle: detection, recovery, and impact on the job, highlighting trade-offs in consistency, availability, and latency.
Pro tip: Emphasize idempotency and checkpointing as key techniques to handle worker failures gracefully, and discuss how Figma's collaborative features might influence the choice of failure handling strategy (e.g., prioritizing availability over strict consistency).
Ask about the job execution framework (e.g., MapReduce, Spark, custom orchestrator) and the desired failure semantics (at-least-once, exactly-once). This sets the context for your answer.
Explain how the system detects a worker node failure, such as heartbeats, timeouts, or missing acknowledgments, and the role of a coordinator or master node.
Describe how the job is recovered: reassigning tasks to healthy workers, replaying from checkpoints, or restarting the entire job. Mention trade-offs like duplicate work vs. data loss.
Discuss the consequences: increased latency, resource wastage, potential inconsistencies, and how the system maintains overall availability and correctness.
Summarize key trade-offs (e.g., exactly-once vs. at-least-once, checkpoint frequency) and how they influence the design, tying back to Figma's needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.