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

Senior
May 2026

Summary

Microsoft system design round, one question, pretty open-ended. Not a lot of context given upfront so you kind of have to drive the whole thing yourself.

Questions Asked (1)

Q1

Design Reddit's homepage.

System DesignProduct Sense & IdeationTechnical Trade-offs
Author's notes

I jumped straight into the feed ranking algorithm and the interviewer had to pull me back to basics.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scope (e.g., scale, read/write patterns, personalization, ranking). Then propose a high-level architecture covering data ingestion, storage, ranking, and serving, and dive into one or two components with trade-offs. Finally, discuss how to handle scale, latency, and reliability.

Pro tip: Emphasize the read-heavy nature and the need for a ranking pipeline that balances freshness and relevance; mention caching strategies and precomputation to meet low-latency requirements.

1. Clarify Requirements

Ask about scale (DAU, posts per day), latency targets, personalization level, and consistency needs. Define functional and non-functional requirements.

2. High-Level Design

Outline the main components: post ingestion, storage (hot/cold), ranking service, feed generation, and API layer. Sketch data flow from post creation to feed display.

3. Deep Dive into Key Components

Choose 1-2 areas to detail, such as ranking algorithm (e.g., collaborative filtering, real-time signals) or storage (e.g., Cassandra for posts, Redis for caching). Discuss trade-offs.

4. Scale and Performance

Explain how to handle millions of users: sharding, replication, caching, CDN, and asynchronous processing. Address latency and throughput.

5. Reliability and Trade-offs

Discuss failure modes, monitoring, and trade-offs between consistency and availability, freshness vs. relevance, and cost vs. performance.

Key Points to Mention

  • Read-heavy workload with high fan-out; use of caching (e.g., Redis, CDN) and precomputed feeds.
  • Ranking pipeline: combining candidate generation (e.g., from subscribed subreddits, trending) with ranking models (e.g., logistic regression, deep learning) using features like upvotes, comments, recency.
  • Storage choices: Cassandra for posts/comments (write-heavy, scalable), Redis for hot data, S3 for media, and possibly Elasticsearch for search.
  • Feed generation strategies: pull vs. push (fan-out on write vs. read), and hybrid approaches for different user types.
  • Scalability: sharding by user or subreddit, replication for fault tolerance, and asynchronous processing with message queues (e.g., Kafka).
  • Trade-offs: consistency vs. availability (CAP), latency vs. freshness, and personalization vs. privacy.

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