Design a Web Crawler: FAANG Interview Question

2025-11-06 · Source: ByteByteGo · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

A web crawler system, essential for AI model training and search engines, needs to process approximately 1 billion pages monthly, equating to 400 pages per second. Designing such a system involves addressing several challenges beyond a simple breadth-first search. Key components include a "polite crawling" mechanism that groups URLs by host into a fixed set of queues (e.g., a few thousand) to manage request rates and avoid overwhelming websites. A "prioritizer" makes the crawler "smarter" by ranking new URLs based on factors like popularity and update frequency, scheduling high-value pages sooner. Redundancy is handled by "URL seen" and "content seen" systems, preventing duplicate crawling. A parser extracts text and links, which are then filtered before being sent back to the prioritizer. Scaling to billions of pages requires distributed crawlers, aggressive DNS caching, and checkpointing for fault tolerance.

Key takeaway

For AI or Machine Learning Engineers designing large-scale data ingestion systems, you must move beyond basic BFS. Implement host-based queuing to ensure polite crawling and avoid IP blocks. Prioritize valuable content using intelligent ranking to optimize resource allocation. You should also integrate "URL seen" and "content seen" systems to efficiently manage redundancy. Consider distributed architectures with aggressive caching and checkpointing to handle fault tolerance and scale effectively.

Key insights

Scaling a web crawler to billions of pages requires distributed, polite, smart, and fault-tolerant design.

Principles

• Group URLs by host to manage request rates.
• Prioritize pages based on value and update frequency.
• Implement redundancy checks for URLs and content.

Method

A web crawler downloads pages, parses HTML, extracts and filters links, then sends new URLs to a prioritizer for scheduling.

In practice

• Use host-based queues to prevent site overload.
• Cache DNS lookups aggressively for performance.
• Implement checkpointing for crash recovery.

Topics

• Web Crawling
• Distributed Systems
• System Design
• Data Ingestion
• URL Prioritization
• Redundancy Detection

Best for: Software Engineer, AI Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by ByteByteGo.