Design a Web Crawler
Design a large-scale web crawler (like Googlebot). Discuss seed URLs, DNS resolution, politeness policies (robots.txt), and dealing with duplicate content.
Why Interviewers Ask This
Interviewers ask this to evaluate your ability to architect distributed systems that balance scale, efficiency, and ethics. They specifically test your understanding of concurrency limits, resource management, and how to handle real-world constraints like politeness policies and duplicate detection in a high-throughput environment.
How to Answer This Question
1. Clarify requirements: Define scope (pages per second), latency goals, and whether it's for search or analytics.
2. High-level architecture: Propose a master-worker pattern with a URL frontier, fetcher workers, and a parser.
3. Core components: Detail the DNS resolver cache, HTTP client with connection pooling, and robots.txt enforcement logic.
4. Deduplication strategy: Explain using a Bloom filter for candidate URLs and an inverted index or consistent hashing for exact duplicates.
5. Politeness & Ethics: Discuss rate limiting per domain, respecting crawl-delay directives, and handling 404s/redirects gracefully.
6. Scalability: Mention sharding the URL frontier and adding more workers dynamically based on load.
Key Points to Cover
- Explicitly mention the Master-Worker architecture for scalability
- Detail the specific implementation of robots.txt parsing and caching
- Explain the trade-offs between Bloom Filters and hash-based deduplication
- Discuss connection pooling and DNS caching to optimize network I/O
- Address how to handle edge cases like infinite loops or server errors
Sample Answer
To design a large-scale crawler like Googlebot, I'd start by defining the goal: indexing billions of pages efficiently while respecting site owners. The core is a distributed system with a central URL Frontier and worker…
Common Mistakes to Avoid
- Ignoring politeness policies like robots.txt, which is critical for ethical crawling
- Failing to explain how the system detects and handles duplicate content at scale
- Overlooking the need for a dedicated URL frontier to manage queue priorities
- Not discussing DNS caching or connection reuse, leading to unrealistic performance estimates
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