rippling·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Jun 2026

Summary

Rippling SWE interview that was basically a full system design mini-project: design and build a backend for an article voting system as a small web API. One question but it sprawled into data modeling, concurrency, scaling, and API design all at once.

Questions Asked (1)

Design and implement the backend for an article voting system exposed as a web API. Articles have an id, author, title, body, and creation timestamp. Users can upvote, downvote, or retract their vote (one vote per user per article, changeable). Provide endpoints for creating articles, fetching by id, voting operations, getting a score, and getting the top-k articles by score. Cover your data model, endpoint design, time complexity per operation, concurrency handling, vote manipulation prevention, and how you'd scale.

System DesignData ModelingAPI & Integrations

Author's notes

This one sprawled fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale assumptions, then present a clean data model and RESTful API design. Walk through each operation's time complexity, explain concurrency control and anti-manipulation measures, and finish with a scaling strategy. Emphasize trade-offs and justify your choices.

Pro tip: Demonstrate awareness of real-world constraints by discussing idempotency and rate limiting, and mention how you'd handle hot articles or celebrity users that cause write contention.

1. Clarify Requirements and Assumptions

Ask about expected scale (users, articles, votes per second), consistency needs, and whether votes are public. State your assumptions clearly to frame the design.

2. Design Data Model and API

Define tables/collections for articles and votes, and outline REST endpoints with methods, paths, and payloads. Include idempotency and error handling.

3. Analyze Operations and Complexity

For each endpoint, specify the time complexity of the underlying operations, considering indexes and data structures. Highlight any potential bottlenecks.

4. Address Concurrency and Integrity

Explain how to handle concurrent votes (e.g., transactions, optimistic locking) and prevent manipulation (authentication, rate limiting, vote validation).

5. Outline Scaling Strategy

Discuss horizontal scaling, caching, sharding, and asynchronous processing for top-k queries. Mention trade-offs between consistency and availability.

Key Points to Mention

Data model: articles table (id, author, title, body, created_at) and votes table (user_id, article_id, vote_type) with unique constraint on (user_id, article_id).
API design: POST /articles, GET /articles/{id}, POST /articles/{id}/vote with body {vote: up|down|retract}, GET /articles/{id}/score, GET /articles/top?k=10.
Time complexity: O(1) for create, fetch, and vote (with indexed lookups); O(log n) or O(1) for score if maintained; O(n log n) for top-k if computed on the fly, or O(k log n) with a heap.
Concurrency: use database transactions with row-level locking or optimistic concurrency control (versioning) to handle simultaneous votes on the same article.
Vote manipulation prevention: require authentication, enforce one vote per user per article via unique constraint, implement rate limiting and CAPTCHA for suspicious activity.
Scaling: cache scores in Redis, use read replicas for fetches, shard votes by article_id, precompute top-k periodically or use a streaming approach with a heap.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.