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Uber·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Uber software engineer interview with a friendly Chinese interviewer who kicked things off in Mandarin before switching to English. Got the Tiny URL system design question, which felt like a bit of a gift compared to what I'd been prepping for.

Questions Asked (1)

Q1

Design a URL shortening service (similar to TinyURL).

System DesignTechnical Trade-offs
Author's notes

Interviewer picked this one and I could tell it was the easier path.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., read/write ratio, expected QPS, latency, storage). Then propose a high-level design with key components like ID generation, database, cache, and API, and dive into trade-offs for each component. Finally, discuss scaling strategies and potential bottlenecks.

Pro tip: At Uber, URL shortening might be used for internal links or promotions, so emphasize reliability, low latency, and analytics. Mention how you'd handle custom aliases and expiration, as these are common in production systems.

1. Clarify Requirements

Ask about scale (e.g., 100M URLs, 1000 QPS), read/write ratio, latency requirements, and features like custom aliases, expiration, and analytics.

2. High-Level Design

Outline components: API servers, ID generator, database, cache, and analytics. Sketch the flow from URL submission to redirection.

3. Deep Dive into Key Components

Discuss ID generation (e.g., base62 encoding of auto-increment or distributed ID), database choice (SQL vs NoSQL), and caching strategy (e.g., Redis).

4. Scaling and Trade-offs

Address scaling: sharding, replication, CDN for redirects, and trade-offs like consistency vs availability, latency vs storage.

5. Wrap Up with Bottlenecks and Extensions

Summarize potential bottlenecks (e.g., ID generation, database writes) and suggest improvements like rate limiting, analytics pipeline, and custom alias handling.

Key Points to Mention

  • ID generation strategies: base62 encoding, distributed ID generators (Snowflake), or hash-based with collision handling.
  • Database choice: SQL for strong consistency vs NoSQL for scalability; consider read-heavy workload and indexing.
  • Caching: Use Redis or Memcached to cache hot URLs and reduce database load.
  • Scaling: Sharding by key, replication for read scalability, and using CDNs for redirects.
  • Trade-offs: Consistency vs availability (CAP theorem), latency vs storage, and cost vs performance.
  • Additional features: Custom aliases, expiration, analytics, and rate limiting.

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