Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026

Summary

Interviewed for a SWE role at Anthropic and got a system design question about building a multi-threaded web crawler. Pretty classic problem but there's a lot of surface area to cover and I don't think I hit all of it.

Questions Asked (1)

Design a web crawler that supports multi-threading.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

I started with the single-threaded version and then layered in concurrency, which felt like the right move, but I fumbled a bit on the coordination piece.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, politeness, deduplication, etc.), then outline a high-level architecture with a URL frontier, fetcher threads, parser, and storage. Dive into concurrency details like thread pool sizing, synchronization, and backpressure, and discuss trade-offs (e.g., BFS vs. priority, in-memory vs. distributed).

Pro tip: Emphasize politeness and robustness: per-domain rate limiting, robots.txt compliance, and handling failures gracefully. This shows you think about real-world constraints, not just concurrency.

1. Clarify Requirements and Scope

Ask about scale (pages, domains), update frequency, politeness constraints, and whether it's distributed. This ensures you design for the right context.

2. High-Level Architecture

Sketch components: URL frontier (queue), fetcher threads, parser, deduplication (visited URLs), and storage. Explain data flow and how threads interact.

3. Concurrency Design

Detail thread pool management, synchronization (locks, concurrent queues), and work distribution. Discuss how to avoid race conditions and deadlocks.

4. Politeness and Robustness

Explain per-domain rate limiting, robots.txt handling, retries with backoff, and error handling. Mention how to avoid overloading servers.

5. Trade-offs and Scaling

Discuss trade-offs: in-memory vs. distributed frontier, BFS vs. priority, thread count vs. I/O. Mention scaling out (e.g., using a message queue) and monitoring.

Key Points to Mention

Thread pool and concurrent data structures (e.g., BlockingQueue) for URL frontier.
Deduplication using Bloom filters or hash sets, with trade-offs on memory vs. accuracy.
Politeness: per-domain rate limiting, robots.txt parsing, and crawl-delay.
Handling dynamic content and JavaScript (if relevant) or focusing on static HTML.
Backpressure and queue management to prevent memory exhaustion.
Distributed crawling: partitioning by domain, using consistent hashing, and coordination.

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