← Anthropic Interview Insights
I went with BFS and a visited set, which felt right.
Start by clarifying requirements and edge cases, then outline a BFS-based crawler using a queue and a visited set to avoid duplicates and cycles. Emphasize URL normalization and error handling for malformed URLs, and conclude with time/space complexity analysis.
Pro tip: Mention that you would use a queue (BFS) to avoid deep recursion and potential stack overflow, and that you would normalize URLs (e.g., lowercase host, remove fragments) to prevent duplicates. Also, discuss politeness policies like rate limiting and robots.txt, showing production awareness.
Ask about scale, concurrency, politeness, and whether the crawler should respect robots.txt. Confirm that only URLs with the same hostname as the start URL should be collected.
Use a queue for BFS and a set for visited URLs. For each page, fetch links, normalize them, filter by hostname, and enqueue if not visited.
Address malformed URLs by catching exceptions and skipping. Normalize URLs to avoid duplicates (e.g., remove fragments, sort query params). Handle cycles via visited set.
Time: O(N + E) where N is number of pages and E is number of links. Space: O(N) for visited set and queue. Discuss worst-case scenarios.
Mention concurrency, distributed crawling, rate limiting, and using a Bloom filter for memory efficiency. Also, consider using a database for visited URLs in large-scale crawls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the high-level architecture: a shared frontier queue with atomic URL claiming, a distributed set of workers, and a per-host rate limiter. Then dive into the key mechanisms for deduplication and rate limiting, discussing trade-offs between consistency, latency, and throughput. Finally, address failure handling and scalability considerations.
Pro tip: Emphasize that deduplication and rate limiting must be atomic and distributed—use Redis with Lua scripts or a similar atomic primitive to avoid race conditions. Also, mention that per-host rate limiting should be enforced at the worker level with a shared token bucket, not just at the scheduler, to handle bursts and failures gracefully.
Ask about scale (URLs per second, number of hosts), consistency requirements (exactly-once vs at-least-once), and whether the crawler is distributed across machines. This sets the stage for design decisions.
Use a centralized queue (e.g., Redis, Kafka) where workers atomically pop URLs. Ensure that a URL is marked as 'in-progress' or 'seen' atomically to prevent duplicate fetches, using transactions or Lua scripts.
Use a distributed token bucket or leaky bucket per host, stored in a shared store like Redis. Workers acquire a token before fetching, and the limiter enforces the rate across all workers.
Design for idempotency: if a worker fails after claiming a URL, the URL should be returned to the queue after a timeout. Use a visibility timeout or a dead-letter queue for failed fetches.
Explain how the system scales horizontally, the trade-offs between strong consistency (e.g., using a database with transactions) and high throughput (e.g., eventual consistency with Redis), and how to monitor and adjust rate limits dynamically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.