Design a Social-Media Comments and Notifications System

Design a comments section for posts in a social-media application with fewer than one million users. Authentication, users, and posts already exist. Cover comment APIs, database design, notifications, and how server and database resources would scale with reads and writes.

Constraints & Assumptions

• Reuse existing identity and post ownership; do not redesign login or the post system.
• Clarify replies, edits, deletion, ordering, and notification recipients before finalizing the schema.
• The user-count ceiling is given, but request rate, activity distribution, and latency targets are not. Estimate them from explicit assumptions if needed.

Clarifying Questions to Ask

• Are replies one level deep or arbitrarily nested?
• Should deleting a parent also delete replies, or leave a tombstone?
• Which actions notify the post author, parent-comment author, or mentioned users?
• Are comments ordered chronologically, by relevance, or both?

Part 1 — APIs and storage

Define create, list, edit, and delete behavior, including authorization and pagination.

What This Part Should Cover

• Comment IDs, post and author references, reply relationships, timestamps, and edit/delete state.
• Stable pagination with indexes matching the chosen order.
• Permission checks and idempotent creation after client retries.

Part 2 — Notifications

Describe how a committed comment produces notifications without making the comment write depend on every downstream delivery.

What This Part Should Cover

• Recipient selection, duplicate suppression, user preferences, and durable event handling.
• Separation of persisted notification state from push/email delivery attempts.
• Recovery after failures between comment commit and event publication.

Part 3 — Scaling reads and writes

Explain where to add capacity and how to diagnose a hot post or overloaded database.

What This Part Should Cover

• Stateless application capacity, bounded queries, caching, and read replicas where appropriate.
• Write-path contention, indexes, hot-key behavior, and cache invalidation.
• Metrics that distinguish a server bottleneck from database or notification backlog pressure.