Design a web crawler in HLD - Scalability & System Analysis

Scalability Analysis - Design a web crawler

Growth Table: Web Crawler Scaling

Users / Scale	100 URLs	10K URLs	1M URLs	100M URLs
Pages to Crawl	100	10,000	1,000,000	100,000,000
Crawler Instances	1	5-10	100-200	10,000+
Storage Needed	MBs	GBs	TBs	Petabytes
Database QPS	10-50	500-1000	10,000+	100,000+
Network Bandwidth	Low	Moderate	High (Gbps)	Very High (Multiple Gbps)
URL Frontier Size	Small	Medium	Large	Very Large (Distributed)

First Bottleneck

The first bottleneck is the URL frontier management and database. As the crawler scales, managing the queue of URLs to visit and storing crawl data grows rapidly. The database can become overwhelmed by high query rates for URL fetching, status updates, and storing page data.

Scaling Solutions

• Horizontal Scaling: Add more crawler instances to distribute crawling load.
• Distributed URL Frontier: Use distributed queues or message brokers to manage URLs efficiently.
• Database Sharding: Partition the database by URL hash or domain to reduce load on single instances.
• Caching: Cache DNS lookups and page content to reduce repeated network calls.
• Politeness and Rate Limiting: Respect site crawl limits to avoid overload and bans.
• Use CDN or Proxy Pools: To distribute network load and avoid IP blocking.
• Incremental Crawling: Prioritize fresh or changed pages to reduce unnecessary crawling.

Back-of-Envelope Cost Analysis

• At 1M URLs, assuming 1 request per page, 10 requests/sec sustained crawling rate.
• Storage: 1M pages * 100KB average = ~100GB storage needed.
• Bandwidth: 10 requests/sec * 100KB = ~1MB/sec (~8Mbps) network usage.
• Database QPS: 10,000+ queries per second for URL status updates and metadata.
• CPU: Multiple crawler instances needed to handle parsing and network IO.

Interview Tip

Start by defining the crawler's main components: URL frontier, fetchers, parsers, storage. Discuss bottlenecks at each scale and propose targeted solutions like sharding, caching, and horizontal scaling. Always mention politeness and real-world constraints like site limits and network bandwidth.

Self Check

Your database handles 1000 QPS. Traffic grows 10x. What do you do first?

Answer: Implement database sharding or add read replicas to distribute load and prevent the database from becoming a bottleneck.

Key Result

The URL frontier and database become the first bottlenecks as crawling scales; distributing URL management and sharding storage are key to scaling efficiently.