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

Senior
May 2026

Summary

System design round at Microsoft for a software engineer role. The whole thing was one big question about designing a web crawler, and they kept pushing on scale and edge cases the entire time.

Questions Asked (1)

Q1

Design a large-scale web crawler that can discover, fetch, parse, and store web pages for use cases like search indexing or content analysis.

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

This is one of those questions where you think you know it until you actually have to draw it out.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., pages per second, storage needs, freshness). Then design a modular architecture covering URL frontier, fetcher, parser, deduplication, and storage, while discussing trade-offs like politeness, scalability, and fault tolerance.

Pro tip: Emphasize the importance of a distributed, fault-tolerant design with back-pressure and monitoring, and mention how you'd handle dynamic content and robots.txt compliance to show production maturity.

1. Clarify Requirements and Scope

Ask about scale (pages/day), content types, freshness, and use case (search indexing vs. content analysis). Define functional and non-functional requirements.

2. High-Level Architecture

Outline components: URL frontier, fetcher, parser, deduplication, storage, and scheduler. Explain how they interact and scale horizontally.

3. Deep Dive into Key Components

Detail the URL frontier (priority, politeness), fetcher (async I/O, rate limiting), parser (HTML parsing, link extraction), and deduplication (content hashing, Bloom filters).

4. Address Scalability and Reliability

Discuss partitioning, replication, fault tolerance, back-pressure, and monitoring. Explain how to handle failures and retries.

5. Discuss Trade-offs and Optimizations

Compare design choices (e.g., push vs. pull, batch vs. stream), and mention optimizations like caching, compression, and dynamic content handling.

Key Points to Mention

  • Distributed architecture with horizontal scaling
  • Politeness and robots.txt compliance
  • Deduplication using content hashing and Bloom filters
  • Storage strategies (e.g., distributed file system, NoSQL)
  • Handling dynamic content and JavaScript rendering
  • Monitoring, metrics, and fault tolerance

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