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

SeniorPrefer not to say

Apr 2026

Summary

Anthropic system design round for a software engineering role, focused entirely on building a web crawler from scratch. The question had a lot of moving parts and I felt like I was playing catch-up the whole time.

Questions Asked (1)

Design and implement a basic web crawler that fetches pages concurrently using a thread executor. Walk through your data structures for the frontier and visited set, how you handle duplicate URLs, and how you'd test and monitor the whole thing.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This question ate me alive a little.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, politeness, scope) and then outline the high-level architecture. Dive into the frontier (e.g., a thread-safe queue) and visited set (e.g., a concurrent hash set with TTL), explaining how you avoid duplicates and ensure thread safety. Finally, discuss testing (unit, integration, load) and monitoring (metrics, logging, alerting) to demonstrate production readiness.

Pro tip: Emphasize politeness and robustness: mention robots.txt, rate limiting per domain, and handling failures gracefully. This shows you think beyond basic functionality and consider real-world constraints, which is crucial for a company like Anthropic that values responsible AI and system reliability.

1. Clarify Requirements and Scope

Ask questions to understand scale (pages per second, total pages), politeness constraints (robots.txt, crawl delay), and whether the crawler is for a specific domain or the entire web. This ensures your design meets the actual needs.

2. Design Data Structures for Frontier and Visited Set

Choose a thread-safe queue (e.g., LinkedBlockingQueue) for the frontier to manage URLs to crawl. For the visited set, use a concurrent hash set (e.g., ConcurrentHashMap.newKeySet()) or a Bloom filter for memory efficiency at scale. Explain how you handle duplicate URLs by checking and adding atomically.

3. Implement Concurrent Fetching with Thread Executor

Use a fixed thread pool (e.g., Executors.newFixedThreadPool) to fetch pages concurrently. Ensure thread safety when accessing shared data structures, and handle exceptions (e.g., timeouts, HTTP errors) without crashing the crawler.

4. Discuss Testing Strategies

Outline unit tests for URL normalization and duplicate detection, integration tests with a mock server, and load tests to verify concurrency and performance. Mention testing edge cases like malformed URLs and network failures.

5. Explain Monitoring and Observability

Describe metrics to track (e.g., pages crawled, queue size, error rates) and how you'd expose them (e.g., via JMX, Prometheus). Discuss logging and alerting for anomalies like sudden drops in crawl rate or high error rates.

Key Points to Mention

Thread safety: use concurrent data structures and atomic operations to avoid race conditions.
Duplicate URL handling: normalize URLs (e.g., remove fragments, sort query params) and use a visited set with atomic check-and-add.
Politeness: respect robots.txt, implement per-domain rate limiting, and set a user-agent.
Backpressure: monitor queue size and adjust thread pool size or use a bounded queue to prevent memory exhaustion.
Fault tolerance: handle exceptions, retry with exponential backoff, and avoid crawling traps (e.g., infinite loops).
Testing: unit tests for URL normalization, integration tests with a local server, and stress tests to validate concurrency.

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