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

SeniorPrefer not to say

Jun 2026

Summary

System design round at Asana for a software engineer role. The whole thing was basically a deep dive into building a scaled-down Twitter, which sounds approachable until you realize how many tricky tradeoffs are buried in it.

Questions Asked (1)

Design a simplified Twitter-like system that supports posting tweets, following/unfollowing users, a home timeline feed, and viewing a user's profile tweets.

System DesignTechnical Trade-offsData Modeling

Author's notes

I started with the data model and APIs which felt like solid footing, but then we got into feed generation and that's where I stumbled a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then estimate scale to drive design decisions. Focus on data modeling for users, tweets, and follows, and design the timeline generation strategy (fan-out on write vs. read) with trade-offs. Finally, discuss storage choices, caching, and how to handle edge cases like celebrity users.

Pro tip: Demonstrate awareness of the read-heavy nature of social feeds and propose a hybrid approach for timeline generation that balances latency and cost, rather than defaulting to a single strategy.

1. Clarify Requirements and Scale

Ask questions to define core features (posting, following, timeline, profile) and non-functional needs (latency, consistency, availability). Estimate scale: number of users, tweets per day, read/write ratio, and fan-out factor.

2. Design Data Model and Storage

Define schemas for users, tweets, and follow relationships. Choose appropriate databases (e.g., SQL for user/follow data, NoSQL or wide-column for tweets) and discuss indexing for efficient lookups.

3. Design Timeline Generation Strategy

Compare fan-out on write (push) vs. fan-out on read (pull) for home timelines. Discuss trade-offs in latency, storage, and complexity, and propose a hybrid approach for scalability.

4. Address Scalability and Performance

Introduce caching (e.g., Redis for timelines), sharding, and asynchronous processing (e.g., message queues for fan-out). Discuss how to handle high-profile users and hot partitions.

5. Discuss Trade-offs and Extensions

Summarize key trade-offs (consistency vs. availability, latency vs. cost) and mention potential extensions like search, notifications, or analytics.

Key Points to Mention

Fan-out on write vs. fan-out on read for timeline generation, including hybrid approaches for celebrity users.
Data modeling: normalized vs. denormalized schemas, and choice of databases (SQL vs. NoSQL) for different access patterns.
Caching strategies for timelines and user profiles to reduce latency and database load.
Sharding and partitioning strategies for tweets and follow graphs to handle scale.
Asynchronous processing using message queues for fan-out and other background tasks.
Trade-offs between consistency, availability, and latency (CAP theorem) in the context of social feeds.

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