Pure Storage·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

Pure Storage system design round, one meaty question about ID allocation that spiraled into a bunch of follow-ups I wasn't fully ready for. The core concept felt familiar but the async and reclaim angles pushed me into territory I had to think through on the fly.

Questions Asked (1)

Design a service with a getID() API that returns a globally unique ID on every call, supporting high concurrency, async clients, and an optional releaseID() operation for reuse.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the obvious stuff: a central counter with a distributed lock.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: scale, latency, durability, and whether IDs must be strictly increasing. Then propose a distributed ID generation service using a combination of timestamp, machine ID, and sequence number (like Snowflake), with an optional ID pool for releaseID(). Discuss trade-offs between centralized and decentralized approaches, and how to handle async clients and high concurrency.

Pro tip: Mention that releaseID() introduces complexity: you need to track released IDs and ensure they aren't reissued while still in use, which may require a lease or timeout mechanism. Also, consider that async clients may batch requests, so the service should support bulk ID allocation.

1. Clarify Requirements and Constraints

Ask about expected QPS, latency requirements, ID format (numeric, string, sortable), durability, and whether IDs can be reused immediately or after a delay. Also clarify if releaseID() is best-effort or must guarantee no duplicates.

2. Choose an ID Generation Strategy

Evaluate options: centralized (e.g., database auto-increment, Redis INCR), decentralized (e.g., Snowflake, UUID), or hybrid. For high concurrency and async, a decentralized approach with a coordination service (e.g., ZooKeeper for worker IDs) is often best.

3. Design the Service Architecture

Outline components: ID generator nodes, a coordination service for worker IDs, and a client library. For async clients, consider a gRPC service with streaming or batching. For releaseID(), design a pool of reusable IDs with a lease mechanism to avoid conflicts.

4. Address Scalability and Fault Tolerance

Discuss how to scale horizontally by adding generator nodes, handle node failures (e.g., worker ID reassignment), and ensure no duplicate IDs. Use consistent hashing or a central allocator for worker IDs.

5. Discuss Trade-offs and Alternatives

Compare with using UUIDs (no coordination but not sortable) or database sequences (simple but bottleneck). Explain why your design meets the requirements and mention potential bottlenecks (e.g., clock skew in Snowflake).

Key Points to Mention

Snowflake algorithm: timestamp + machine ID + sequence number, with clock synchronization considerations.
Coordination service (e.g., ZooKeeper, etcd) for assigning unique worker IDs to generator nodes.
Async client support: gRPC with streaming, batching, or client-side caching of ID ranges.
releaseID() implementation: ID pool with lease/timeout to prevent reuse while in use, and garbage collection of expired leases.
High concurrency: lock-free or sharded counters, or pre-allocated ID ranges per node.
Trade-offs: centralized vs decentralized, latency vs uniqueness guarantees, and complexity of releaseID().

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