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Microsoft·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate
May 2026

Summary

Microsoft SWE interview with a system design question. Just one prompt but it covers a lot of ground if you let it.

Questions Asked (1)

Q1

Design a service like PasteBin.

System DesignTechnical Trade-offsData Modeling
Author's notes

Spent the first few minutes on the obvious stuff, URL shortening, storage, read vs write ratios.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then estimate scale (e.g., number of pastes per day, read/write ratio). Propose a high-level architecture with key components (API, storage, cache, CDN) and dive into data modeling, storage choices, and trade-offs. Discuss scalability, reliability, and cost optimizations.

Pro tip: Emphasize the read-heavy nature of PasteBin and propose caching and CDN strategies to handle high read traffic efficiently. Also, discuss how to handle large pastes and expiration policies.

1. Requirements Clarification

Ask about expected scale, read/write ratio, paste size limits, expiration, and access patterns (public/private). Clarify non-functional requirements like latency, availability, and durability.

2. High-Level Design

Sketch the main components: client, API gateway, application servers, storage (SQL/NoSQL), cache, and CDN. Explain the flow for creating and retrieving pastes.

3. Data Modeling and Storage

Design the schema for pastes (e.g., paste_id, content, expiration, metadata). Choose storage: object storage for content, database for metadata. Discuss indexing and sharding.

4. Scalability and Performance

Address how to handle high read traffic with caching (Redis/Memcached) and CDN. Discuss write scalability, database partitioning, and replication.

5. Trade-offs and Optimizations

Discuss trade-offs: SQL vs NoSQL, consistency vs availability, cost vs performance. Mention optimizations like compression, deduplication, and garbage collection for expired pastes.

Key Points to Mention

  • Read-heavy workload: optimize for reads with caching and CDN.
  • Storage choices: object storage (e.g., Azure Blob) for content, database for metadata.
  • Expiration and cleanup: TTL, background jobs for deletion.
  • Scalability: horizontal scaling, sharding, replication.
  • Security: access control, encryption, rate limiting.
  • Cost optimization: tiered storage, compression.

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