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

Senior
Apr 2026

Summary

System design round at Anthropic for a software engineer role. The web crawler question sounds deceptively straightforward but there's a lot of surface area once you start pulling on the concurrency and politeness threads.

Questions Asked (1)

Q1

Design a web crawler that starts from a seed URL, fetches pages, extracts hyperlinks, and recursively crawls them without revisiting the same URL twice.

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

I started with BFS because it felt safer to defend and the interviewer seemed fine with that.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then outline a high-level architecture with a URL frontier, fetcher, parser, and deduplication mechanism. Dive into the core components, discuss trade-offs (e.g., politeness, scalability, robustness), and wrap up with failure handling and monitoring.

Pro tip: Emphasize politeness and robustness early—mention robots.txt, rate limiting, and handling dynamic content—because production crawlers must respect site policies and adapt to real-world messiness.

1. Clarify Requirements and Scale

Ask about expected scale (pages per second, total pages), content types, freshness, and constraints like politeness or budget. This shapes design decisions.

2. High-Level Architecture

Sketch components: URL frontier (queue), fetcher (HTTP client), parser (HTML link extractor), deduplication (visited set), and storage. Explain data flow from seed URL.

3. Core Components Deep Dive

Detail each component: frontier prioritization, distributed fetching, dedup using Bloom filters or distributed sets, and parsing with link normalization.

4. Trade-offs and Scalability

Discuss trade-offs: BFS vs. priority, in-memory vs. persistent dedup, single vs. distributed crawler. Address scaling with sharding, partitioning, and async I/O.

5. Robustness and Politeness

Cover error handling (retries, timeouts), robots.txt compliance, rate limiting per domain, and handling dynamic content (JS rendering).

Key Points to Mention

  • URL deduplication techniques: Bloom filters, distributed sets, and normalization (e.g., removing fragments, lowercasing).
  • Politeness: robots.txt, crawl-delay, rate limiting per domain to avoid overwhelming servers.
  • Scalability: distributed crawling with sharding by domain, message queues (e.g., Kafka), and async I/O.
  • Robustness: handling failures (retries, exponential backoff), timeouts, and dynamic content (headless browsers).
  • Frontier management: priority queues, BFS vs. priority-based crawling, and freshness policies.
  • Storage and indexing: storing fetched pages, extracted links, and metadata for analysis or search.

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