Anthropic·Software Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

Anthropic coding round, got a multithreading problem that looked deceptively like a standard BFS until you realize half the complexity is just keeping things thread-safe and not deadlocking yourself into a corner.

Questions Asked (1)

Implement a multithreaded web crawler. Given a start URL and a function that returns all URLs reachable from a page, crawl every reachable URL sharing the same hostname. Each URL should be visited at most once, and you must use multiple threads to fetch pages concurrently.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with the hostname filtering which felt easy, just split on slashes and compare.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a design using a thread-safe queue for URLs, a shared visited set with synchronization, and a thread pool for concurrent fetching. Discuss trade-offs like synchronization overhead, politeness, and error handling, and consider optimizations like per-host queues or async I/O.

Pro tip: Emphasize correctness under concurrency by explaining how you prevent duplicate visits and handle race conditions, and mention that you would test with a mock fetch function to simulate delays and failures.

1. Clarify Requirements and Constraints

Ask about expected scale, politeness policies, error handling, and whether the crawler should be breadth-first or depth-first. Confirm that only URLs with the same hostname as the start URL should be crawled.

2. Design Core Data Structures

Propose a thread-safe queue (e.g., BlockingQueue) for URLs to visit, a concurrent set (e.g., ConcurrentHashMap.newKeySet()) for visited URLs, and a thread pool (e.g., ExecutorService) to manage worker threads.

3. Define Worker Logic and Synchronization

Each worker thread takes a URL from the queue, checks if it's already visited (atomically adding to the visited set), fetches the page, extracts links, and enqueues new same-host URLs. Use synchronization or atomic operations to avoid race conditions.

4. Handle Termination and Edge Cases

Use a counter or poison pill to detect when all work is done and shut down threads gracefully. Handle exceptions, timeouts, and redirects, and ensure the crawler doesn't hang if the queue is empty but threads are still active.

5. Discuss Trade-offs and Optimizations

Talk about trade-offs between thread pool size and throughput, synchronization overhead, and politeness (rate limiting). Mention alternatives like asynchronous I/O (e.g., asyncio) or per-host queues for better scalability.

Key Points to Mention

Thread-safe data structures: concurrent queue and set to avoid race conditions.
Atomic check-and-add for visited URLs to ensure each URL is visited once.
Thread pool management: fixed size, dynamic scaling, and graceful shutdown.
Error handling: retries, timeouts, and logging for failed fetches.
Politeness: rate limiting per host to avoid overwhelming servers.
Scalability: potential bottlenecks and alternatives like async I/O or distributed crawling.

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