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

SeniorPrefer not to say
Jun 2026

Summary

Bytedance SRE interview that was basically a system design session dressed up as a coding round. They wanted a full TinyURL implementation with real discussion about trade-offs, not just pseudocode.

Questions Asked (1)

Q1

Design and implement a TinyURL service with encode and decode functions. Walk through your short-code generation strategy, collision handling, storage choices, and TTL support.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This one sprawled in every direction.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, read/write ratio, TTL, custom aliases) and then propose a high-level design with a load balancer, application servers, and a distributed database. Discuss short-code generation strategies (e.g., base62 encoding of a unique ID, hash-based, or pre-generated keys), collision handling, storage schema, and TTL implementation. Conclude with trade-offs and potential optimizations.

Pro tip: Emphasize the read-heavy nature of the service and propose caching (e.g., Redis) for hot URLs, and consider using a distributed counter or Snowflake IDs to avoid collisions in a distributed environment.

1. Clarify Requirements

Ask about expected scale (e.g., 100M URLs, 1000 QPS), read/write ratio, TTL requirements, custom aliases, and analytics. This shapes design decisions.

2. High-Level Design

Outline components: client, load balancer, application servers, database, cache. Explain the flow for encode (write) and decode (read).

3. Short-Code Generation

Discuss strategies: base62 encoding of a unique ID (from a distributed counter or Snowflake), hash-based (MD5/SHA256) with collision resolution, or pre-generated key service. Compare trade-offs.

4. Storage and TTL

Choose a database (e.g., NoSQL like Cassandra for scalability, or SQL for ACID). Design schema: short_code (PK), original_url, creation_time, expiration_time. Implement TTL via lazy deletion or background cleanup.

5. Collision Handling and Scalability

Explain how to handle collisions (e.g., retry with new ID, use unique constraint). Discuss scaling: sharding, replication, caching, and rate limiting.

Key Points to Mention

  • Base62 encoding of a unique ID (e.g., from a distributed counter or Snowflake) to generate short codes.
  • Collision handling: use a unique constraint and retry, or pre-allocate ranges to avoid collisions.
  • Storage: NoSQL (e.g., Cassandra) for scalability and TTL support, or SQL with indexing.
  • TTL implementation: store expiration timestamp and use lazy deletion or a background job to clean up.
  • Caching: use Redis or Memcached to cache hot URLs and reduce database load.
  • Trade-offs: consistency vs. availability, latency vs. storage, and custom alias support.

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