# Design a Web Crawler: FAANG Interview Question

Source: https://www.youtube.com/watch?v=6u25GckPhLU
Recap page: https://rapidrecap.app/video/6u25GckPhLU
Generated: 2025-11-10T21:40:24.47+00:00

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## Quick Overview

Designing a robust, scalable web crawler requires addressing several complex challenges, including managing massive data volumes (billions of pages), ensuring politeness to avoid overloading hosts, implementing intelligent prioritization, avoiding duplicate content via URL and content seen checks, handling HTML parsing, and distributing the workload geographically for performance.

**Key Points:**
- The web crawler must handle massive scale, aiming for 400 pages per second, which translates to crawling one billion pages every month.
- Politeness is maintained by using a host-based queue system (e.g., B1, B2, Bn) managed by a Queue Router and Selector, ensuring requests to the same host are delayed by at least 2 seconds (1 req/2 sec).
- Prioritization is achieved by introducing a Prioritizer that routes URLs into different priority queues (f1, f2, fn) based on factors like page popularity, update frequency, and the number of incoming links.
- Duplicate avoidance uses two primary checks: a 'URL Seen' system (likely using a Bloom filter or hash set) and a 'Content Seen' system that hashes content to prevent crawling mirrored content.
- The core architecture flows from Seed URLs through a URL Frontier, HTML Downloader (which uses a DNS Resolver cache), Content Parser, and Link Extractor, before new links feed back into the URL Frontier.
- Scaling to billions necessitates distributing crawlers across regions geographically close to target servers, using local storage/queues, and implementing consistent checkpointing to allow recovery after crashes.

![Screenshot at 03:24: The comprehensive system architecture diagram illustrates the flow: Input URLs pass through a Prioritizer which distributes them across multiple priority queues \(f1 to fn\), managed by a Front Queue Selector, then routed via a Back Queue Router \(using a Mapping Table\) to worker threads, balancing crawling scope with politeness and prioritization.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-03-24.png)

**Context:** This video provides a system design walkthrough for building a large-scale web crawler capable of indexing billions of web pages, a task undertaken by major search engines like Google, OpenAI, and Anthropic. The design focuses on handling high throughput (400 pages/sec), maintaining politeness towards web servers, intelligent prioritization of important pages, and robust mechanisms for deduplication and fault tolerance.

## Detailed Analysis

The design of a high-scale web crawler begins with defining requirements: crawling one billion pages per month, equating to 400 pages per second, while ensuring the process is fast and distributed. The system must incorporate several key components to manage this load. First, politeness is enforced by maintaining separate queues (B1, B2, Bn) per host, managed by a Queue Router and Selector, which enforces a minimum delay, such as 1 request every 2 seconds, between requests to the same host. Second, prioritization is added via a Prioritizer component that routes incoming URLs into different priority queues (f1, f2, fn) based on factors like page popularity, update frequency, and incoming link count, ensuring high-value pages are crawled sooner. The core pipeline involves Seed URLs feeding the URL Frontier, followed by an HTML Downloader that relies on a cached DNS Resolver to speed up lookups. After downloading, the Content Parser extracts links, which are then checked against 'URL Seen' and 'Content Seen' systems to avoid processing duplicates. The 'URL Seen' system checks if the URL has been encountered before, while the 'Content Seen' system hashes the document content to detect mirrored pages, even if the URLs differ. New, unique URLs are filtered and sent back to the URL Frontier. For massive scale, the system must be geographically distributed, with regional crawlers maintaining politeness across multiple target servers, and reliable checkpointing must be implemented to save the current state (current state vs. checkpoint) allowing recovery if a crawler fails.

### Initial Requirements & Scale

- Target is 1 billion pages/month (400 pages/sec)
- Must be fast and distributed
- Initial crawling involves a simple BFS of seed URLs.

### Handling Politeness

- Enforce minimum delay (1 req/2 sec) between requests to the same host
- Use a host-based queue system (B1, B2, Bn) managed by a Queue Router and Selector.

### Prioritizing Crawl Order

- Introduce a Prioritizer to sort URLs into different priority queues (f1, f2, fn)
- Prioritization factors include page popularity, update frequency, and page link count.

### Core Pipeline Components

- Seed URLs -> URL Frontier -> HTML Downloader (using DNS Resolver cache) -> Content Parser -> Link Extractor -> URL Filter -> URL Seen check (loops back to Frontier).

### Duplicate Avoidance

- Utilize a 'URL Seen' system to track visited URLs and a 'Content Seen' system that hashes content to detect mirrored articles or duplicate content.

### Scaling Strategy

- Distribute crawlers geographically close to target servers to reduce latency and maintain politeness across regions
- Implement regular checkpointing to save the current state for fault tolerance and recovery.

![Screenshot at 00:04: Initial diagram showing the concept of an AI company model using massive web data collection.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-00-04.png)
![Screenshot at 00:30: Defining the scale: 1 billion pages/month, requiring a performance of 400 pages/sec.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-00-30.png)
![Screenshot at 00:50: Visual representation of handling failures, showing that the system must continue operating even when some paths fail \(marked with X\).](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-00-50.png)
![Screenshot at 01:11: Clerk advertisement showcasing pre-built UI components for authentication and user management.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-01-11.png)
![Screenshot at 02:21: Introduction to politeness handling: URLs are grouped by host into separate queues \(B1 to Bn\) before being processed.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-02-21.png)
![Screenshot at 02:59: The Prioritizer component routes URLs into different queues \(f1, f2, fn\) based on metrics like page popularity and update frequency.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-02-59.png)
![Screenshot at 03:39: Duplicate avoidance components: URL Seen check \(using funnel/hash set\) and Content Seen check \(using document hashing\) prevent redundant crawling.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-03-39.png)
![Screenshot at 04:23: The full high-level pipeline diagram showing the flow from Seed URLs to Content Storage, including DNS resolution and link extraction.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-04-23.png)
![Screenshot at 04:53: Fault tolerance mechanism illustrated by comparing 'Current State' \(red server\) versus 'Checkpoint' \(flagged server\) for recovery.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-04-53.png)
![Screenshot at 05:06: Final overview diagram summarizing all major components: distributed crawlers, politeness queues, prioritization logic, deduplication, and storage.](https://ss.rapidrecap.app/screens/6u25GckPhLU/00-05-06.png)