I started with a min-heap keyed on send_at and they seemed fine with that, but I fumbled a bit when they pushed on cancellation.
Start by clarifying requirements and scale, then design a data model that supports one-shot and recurring emails with cancellation and per-user listing. Propose a dispatcher architecture that efficiently finds due emails, handles failures, and scales horizontally, discussing trade-offs at each step.
Pro tip: Emphasize idempotency and exactly-once semantics for email sends, as duplicate or missed emails are critical issues in production. Also, discuss how to handle time zones and daylight saving for recurring schedules.
Ask about expected volume (emails per second), latency requirements, and whether recurring emails need complex rules (e.g., cron expressions). Confirm if cancellation should be immediate and if listing pending emails needs pagination.
Propose a schema for emails: id, user_id, recipient, subject, body, send_time, recurrence_rule (nullable), status (pending/sent/cancelled), and next_send_time for recurring. Consider indexing on (status, next_send_time) and (user_id, status) for efficient queries.
Describe a dispatcher service that polls a queue or database for due emails. Use a priority queue or time-based partitioning to efficiently find due emails. For recurring emails, after sending, compute the next occurrence and update next_send_time.
For cancellation, update status to cancelled and remove from scheduling structures. For listing pending emails per user, query by user_id and status=pending, with pagination. Ensure consistency between database and any in-memory queues.
Discuss idempotency (e.g., using a unique email id to prevent duplicates), retries with exponential backoff, dead-letter queues, and horizontal scaling of dispatchers. Consider using a distributed lock or leader election for scheduling.
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Start by clarifying the requirements: exactly-once semantics for email dispatch, tolerance for clock skew, and failure handling. Then propose a design that combines idempotency keys, a distributed lock or lease, and a deduplication store with TTL, while addressing retries via exponential backoff and dead-letter queues. Finally, discuss trade-offs between consistency, latency, and complexity, and how to monitor and reconcile discrepancies.
Pro tip: Emphasize that true exactly-once delivery is impossible in distributed systems; instead, aim for effectively-once by making the consumer idempotent and using a deduplication window. Mention that Stripe often values pragmatic solutions that balance correctness and operational simplicity.
Ask about the expected scale, acceptable latency, and whether duplicate emails are worse than dropped emails. Confirm if the system must guarantee exactly-once or if at-least-once with idempotency is acceptable.
Assign a unique idempotency key to each email request (e.g., based on user ID, email type, and timestamp). Before sending, check a deduplication store (e.g., Redis with TTL) to see if the key was already processed; if so, skip sending.
Use a logical clock or a centralized timestamp service (e.g., Google TrueTime) to avoid relying on local clocks. For retries, include the original timestamp and use a monotonic sequence number to detect out-of-order or stale requests.
On failure, retry with exponential backoff and jitter, up to a max attempts. If still failing, move the message to a dead-letter queue for manual inspection. Ensure retries are idempotent by reusing the same idempotency key.
Set up metrics for duplicate sends, dropped emails, and retry counts. Periodically reconcile the deduplication store with the email provider's logs to detect and fix inconsistencies. Discuss trade-offs and potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the distributed setup, such as scale, consistency needs, and failure tolerance. Then, systematically address the key challenges: partitioning work, coordination, fault tolerance, and consistency. Finally, discuss trade-offs of different approaches and how they impact the overall design.
Pro tip: Emphasize idempotency and exactly-once semantics for critical operations like payments, and discuss how you would handle partial failures without disrupting the system. This shows you understand Stripe's domain and the importance of reliability.
Ask questions to understand the scale, latency, consistency, and availability requirements. Determine if the scheduler needs to be highly available, partition-tolerant, and what consistency model is acceptable.
Enumerate the key challenges: coordination, leader election, work distribution, fault tolerance, and consistency. Explain how these differ from a single-node scheduler.
Outline a distributed architecture, such as a leader-based scheduler with worker nodes, or a decentralized approach using consistent hashing. Discuss how tasks are assigned and monitored.
Describe mechanisms for handling node failures, such as replication, heartbeats, and failover. Discuss how to achieve exactly-once semantics and idempotency for critical tasks.
Compare different approaches (e.g., centralized vs. decentralized, strong vs. eventual consistency) and explain the trade-offs in terms of complexity, performance, and reliability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.