Design a Web Crawler

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 nodes. The Frontier manages the queue of URLs to visit, prioritized by freshness and authority. Workers pull URLs, resolve DNS with a local cache to reduce latency, and fetch content via HTTP clients that respect connection limits. Crucially, we must enforce politeness. Before fetching, the system checks a cached version of robots.txt for the target domain. If a path is disallowed or a crawl-delay exists, we skip or throttle requests accordingly. This prevents overloading servers and maintains trust. For duplicate content, we use a two-tier approach. First, a Bloom filter quickly filters out obviously repeated URLs. Second, for deeper analysis, we compute hashes of page bodies and compare them against a distributed key-value store to identify near-duplicates. We also handle redirects and 404s by updating the frontier state immediately. Finally, the parsed data goes into an inverted index for search retrieval. To scale, we shard the URL Frontier across multiple machines and add more workers as needed, ensuring the system remains responsive even during peak traffic.