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

SeniorPrefer not to say
May 2026

Summary

Bytedance system design round for a software engineer role. The whole session was basically one big deep-dive into URL shortening, which sounds simple but they kept pulling threads until I was sweating over cache invalidation and collision rates.

Questions Asked (1)

Q1

Design a URL shortening service like TinyURL that can generate short links and redirect users back to the original URLs at scale.

System DesignTechnical Trade-offsData Modeling
Author's notes

I started with the API surface (create short link, resolve short link) which felt like the right move, but I spent too long there and had to rush through the storage layer.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, read/write ratio, latency, custom aliases, expiration) and then walk through the high-level design: API, data model, key generation, and redirection flow. Dive into the critical components like encoding strategy, storage choice, caching, and scaling, discussing trade-offs at each step.

Pro tip: Emphasize the read-heavy nature (100:1 read/write) and how it drives design decisions like caching and read replicas; also mention that you'd monitor key metrics like cache hit rate and redirect latency to ensure performance.

1. Clarify Requirements and Constraints

Ask about expected scale (e.g., 100M URLs, 10K QPS), read/write ratio, latency requirements, and features like custom aliases, expiration, and analytics. This sets the scope and guides design decisions.

2. High-Level Design and API

Define the core APIs: POST /shorten to create a short URL, GET /{shortKey} to redirect. Sketch the components: load balancer, web servers, key generation service, database, cache, and analytics.

3. Data Model and Key Generation

Choose a storage solution (e.g., NoSQL like Cassandra for scalability) and design the schema: shortKey -> longURL, with metadata. Discuss key generation strategies: base62 encoding of a unique ID (e.g., from a distributed counter like Snowflake) or hash-based with collision handling.

4. Scaling and Performance

Address read-heavy traffic with caching (e.g., Redis) and read replicas. For writes, consider sharding by shortKey. Discuss trade-offs: consistency vs. availability, cache eviction policies, and handling hot keys.

5. Trade-offs and Extensions

Summarize key trade-offs (e.g., SQL vs. NoSQL, centralized vs. distributed key generation). Mention potential extensions: custom aliases, expiration, analytics, and rate limiting.

Key Points to Mention

  • Base62 encoding of a unique ID (e.g., from a distributed counter like Snowflake) to generate short keys, ensuring uniqueness and short length.
  • Caching strategy: use Redis or Memcached to cache hot URLs, reducing database load and latency; discuss cache eviction policies like LRU.
  • Database choice: NoSQL (e.g., Cassandra) for scalability and high write throughput, with sharding by shortKey to distribute load.
  • Handling collisions: if using hash-based approach, detect and resolve collisions (e.g., by appending a counter or rehashing).
  • Read-heavy optimization: use read replicas, CDN for static assets, and consider eventual consistency for analytics.
  • Monitoring and metrics: track QPS, latency, cache hit rate, and error rates to ensure system health and identify bottlenecks.

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