Design a Web Crawler: FAANG Interview Question

Key Concepts

• Web Crawler: A system that systematically browses the internet to collect data.
• Breadth-First Search (BFS): A graph traversal algorithm that explores neighbor nodes before moving to the next level.
• Politeness: The practice of not overwhelming websites with too many requests.
• Host: A specific website or server.
• Queue: A data structure used to store URLs waiting to be crawled.
• Hashing: A technique to map data to a fixed-size value, used here to assign hosts to queues.
• Prioritizer: A component that ranks URLs based on their perceived value.
• Frontier: The list of pages waiting to be crawled.
• URL Seen: A system to prevent crawling the same link multiple times.
• Content Seen: A system to detect and avoid duplicate pages.
• Parser: A component that validates HTML, extracts text, and identifies links.
• Link Extractor: A component that finds new URLs from a page.
• URL Filter: A component that removes unwanted links (e.g., images, videos).
• Distributed Crawlers: Multiple crawlers working in parallel across different regions.
• DNS Lookup: The process of translating a domain name into an IP address.
• Checkpointing: Saving the state of a crawler to allow for restarts after failures.

Designing a Scalable Web Crawler

This document outlines the design considerations for a web crawler capable of handling billions of pages per month, which translates to approximately 400 pages per second. The core challenges lie in achieving speed, distribution, politeness, intelligent prioritization, and resilience to failures.

1. Initial Approach and Its Limitations

A basic Breadth-First Search (BFS) approach is a starting point. It involves:

• Initializing a queue with seed URLs.
• Visiting URLs from the queue.
• Fetching the content of each page.
• Extracting new links from the fetched page.
• Adding these new links back to the queue.

However, this simple BFS approach breaks down at scale due to two primary issues:

• Overwhelming Hosts: Most pages link to other pages on the same host (e.g., Wikipedia). A single queue would lead to excessive requests to the same website, potentially causing the crawler to be blocked due to rate limits.
• Unequal Page Value: Not all pages are equally important. The homepage of a major company is more valuable than a random forum post. Treating all URLs equally is inefficient.

2. Achieving Politeness

To address the issue of overwhelming hosts, the crawler must be polite. This is achieved by:

• Host-Based Queues: Instead of one large queue, URLs are grouped by their host.
• Fixed Number of Queues: A fixed set of queues (e.g., a few thousand) is maintained.
• Host-to-Queue Mapping: A simple hashing function maps each host to one of these queues. All URLs from the same website are directed to the same queue.
• Delayed Requests: Worker threads pull URLs from these queues with controlled delays between requests to each host. This ensures that no single website is bombarded with requests.

3. Intelligent Prioritization

To crawl the web strategically rather than randomly, a prioritization layer is introduced:

• Ranking URLs: When new URLs are discovered, a prioritizer ranks them based on factors such as:
  • Page popularity.
  • Frequency of updates.
  • Number of incoming links from other sites.
• The Frontier: Ranked URLs are placed in the frontier, which is essentially the list of pages waiting to be crawled.
• Scheduling: High-value URLs are scheduled for crawling sooner, while low-value ones wait longer.
• Advanced Models: In large-scale crawlers, prioritization can involve sophisticated models, including machine learning models that adapt in real-time.

4. Handling Redundancy

The internet contains duplicate content. To manage this, two systems are implemented:

• URL Seen: This system prevents the crawler from adding and processing the same URL multiple times.
• Content Seen: This system detects duplicate pages by generating a hash of the page's text or structure. If a page's hash matches an already processed page, it is considered a duplicate and skipped.

5. The Crawling Pipeline

Once a page is selected for crawling, it goes through a pipeline:

1. Download: The page content is fetched.
2. Parser:
   • Validates the HTML structure.
   • Extracts useful text content.
   • Identifies links to follow.
3. Link Extractor:
   • Finds new URLs within the parsed page.
   • Converts relative links to absolute links.
   • Sends these new URLs to the prioritizer.
4. URL Filter:
   • Removes unwanted links, such as those pointing to image files, video files, or disallowed domains.
5. Repeat: The process loops, with new, filtered, and prioritized URLs being added back to the system.

6. Scaling to Billions of Pages

To achieve the scale of crawling billions of pages, several additional considerations are crucial:

• Distributed Crawlers: Multiple crawlers are deployed across different geographical regions. Each crawler manages a portion of the frontier, often located geographically close to the target servers to reduce latency.
• Distributed Politeness: Maintaining politeness across multiple distributed crawlers requires careful coordination to avoid collectively overwhelming a single host.
• Performance Bottlenecks:
  • DNS Lookups: These can be slow. Aggressive caching of DNS records is employed to speed up this process.
• Resilience and Checkpointing:
  • Checkpointing: The state of each crawler is periodically saved. If a crawler crashes, it can restart from its last saved checkpoint, preventing loss of progress.

7. Conclusion

The design of a large-scale web crawler is a complex endeavor that moves beyond a simple download-and-follow loop. It evolves into a sophisticated, distributed system that meticulously balances fairness (politeness), scale (handling billions of pages), and intelligence (prioritization). This system operates quietly behind the scenes, mapping the vast expanse of the internet.

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