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

SeniorPrefer not to say
Jun 2026

Summary

Dropbox SWE interview with a system design question about ID management. Pretty focused problem, not a lot of fluff around it.

Questions Asked (1)

Q1

Design a module that allocates unique User IDs from a shared pool and reclaims them when they're released back for reuse.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

My first instinct was a simple counter but they pushed back pretty fast on that.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, persistence, concurrency, ID format) and then propose a design that balances simplicity, performance, and reliability. Walk through the core data structures and algorithms for allocation and reclamation, then discuss trade-offs and failure handling.

Pro tip: Mention that ID reclamation can lead to security and debugging issues (e.g., stale references), so consider a generation counter or delayed reuse. Also, discuss how to handle ID exhaustion and whether IDs should be globally unique or just unique within a pool.

1. Clarify Requirements

Ask about scale (IDs per second, total pool size), persistence needs, concurrency, and whether IDs must be globally unique or can be reused immediately. Also clarify if IDs need to be numeric, sequential, or can be arbitrary.

2. High-Level Design

Propose a module with two main operations: allocate() and release(id). Outline the components: an ID generator, a free list (or pool) for reclaimed IDs, and a persistent store if needed. Discuss whether to use a centralized service or a distributed approach.

3. Data Structures & Algorithms

Detail how to efficiently allocate and reclaim IDs. For allocation, consider using a queue or stack for free IDs, and a counter for new IDs. For reclamation, add the ID back to the free list. Discuss thread-safety using locks or lock-free structures.

4. Trade-offs & Scalability

Compare approaches: in-memory vs. persistent, centralized vs. distributed, immediate reuse vs. delayed reuse. Discuss trade-offs in terms of latency, throughput, consistency, and complexity. Mention how to handle ID exhaustion and failure recovery.

5. Edge Cases & Reliability

Cover edge cases: double release, releasing invalid IDs, concurrent allocation/release, and system crashes. Propose solutions like idempotent release, validation, and write-ahead logging for persistence.

Key Points to Mention

  • Concurrency control: locks, atomic operations, or lock-free data structures to ensure thread-safe allocation and release.
  • Persistence: using a database or distributed store to survive restarts, and how to recover the free list.
  • ID reuse policies: immediate vs. delayed reuse to avoid stale references, and using generation numbers.
  • Scalability: sharding the ID space or using a distributed counter (e.g., Snowflake) for high throughput.
  • Failure handling: idempotent release, handling double-free, and crash recovery.
  • Monitoring and metrics: tracking allocation rate, free list size, and exhaustion warnings.

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