Design A Web Crawler

This chapter delves into the design of a web crawler, a classic system design interview question.

A web crawler, also known as a robot or spider, is commonly used by search engines to discover new or updated web content, including web pages, images, videos, and PDF files. It operates by collecting an initial set of web pages and then following links on those pages to gather more content.

(Figure 1: Visual example of the crawl process)

Crawlers serve various purposes:

  • Search engine indexing: Collecting web pages to build a local index for search engines (e.g., Googlebot).
  • Web archiving: Preserving web data for future use (e.g., national libraries archiving websites).
  • Web mining: Extracting valuable knowledge from the internet, such as analyzing company reports.
  • Web monitoring: Detecting copyright and trademark infringements.

The complexity of a web crawler varies with its intended scale. This design will focus on a scalable solution.

Step 1: Understanding the Problem and Design Scope

The fundamental algorithm of a web crawler involves:

  1. Downloading web pages from a given set of URLs.
  2. Extracting new URLs from these downloaded pages.
  3. Adding new URLs to the download list and repeating the process.

Designing a highly scalable web crawler is complex. We need to clarify requirements and define the design scope.

Clarification Questions & Assumptions:

  • Main purpose: Search engine indexing.
  • Pages per month: 1 billion web pages.
  • Content types: HTML only.
  • New/edited pages: Yes, should be considered.
  • Storage: Store HTML pages for up to 5 years.
  • Duplicate content: Ignore pages with duplicate content.

Characteristics of a Good Web Crawler:

  • Scalability: Efficiently crawl billions of pages using parallelization.
  • Robustness: Handle bad HTML, unresponsive servers, crashes, and malicious links gracefully.
  • Politeness: Avoid sending excessive requests to a single website within a short period.
  • Extensibility: Flexible design to support new content types with minimal changes.

Back-of-the-Envelope Estimation:

  • QPS (Pages per second): 1 billion pages / 30 days / 24 hours / 3600 seconds ≈ 400 pages/second.
  • Peak QPS: 2 * QPS = 800 pages/second.
  • Average web page size: 500 KB.
  • Storage per month: 1 billion pages * 500 KB = 500 TB.
  • Total storage (5 years): 500 TB/month * 12 months/year * 5 years = 30 PB.

Step 2: High-Level Design

Our high-level design includes several key components:

(Figure 2: High-level design diagram of a web crawler)

  • Seed URLs: Starting points for the crawl. Selection strategies include locality (e.g., by country) or topics (e.g., shopping, sports).

  • URL Frontier: Stores URLs to be downloaded, typically acting as a FIFO queue.

  • HTML Downloader: Downloads web pages from the internet using URLs from the URL Frontier.

  • DNS Resolver: Translates URLs into IP addresses for the HTML Downloader.

  • Content Parser: Parses and validates downloaded web pages to handle malformed content.

  • Content Seen?: A data structure (e.g., using hash values of pages) to detect and eliminate duplicate content.

  • Content Storage: Stores HTML content, primarily on disk for large datasets, with popular content cached in memory.

  • URL Extractor: Parses HTML pages to extract new links. Relative paths are converted to absolute URLs.

    (Figure 3: Example of a link extraction process)

  • URL Filter: Excludes specific content types, file extensions, error links, and blacklisted sites.

  • URL Seen?: A data structure (e.g., Bloom filter or hash table) to track visited URLs or URLs already in the Frontier, preventing redundant processing and infinite loops.

  • URL Storage: Stores URLs that have already been visited.

Web Crawler Workflow (illustrated with sequence numbers):

(Figure 4: Web crawler workflow diagram)

  1. Seed URLs are added to the URL Frontier.
  2. HTML Downloader fetches a list of URLs from the URL Frontier.
  3. HTML Downloader obtains IP addresses from the DNS resolver and starts downloading pages.
  4. Content Parser parses HTML pages and checks for malformation.
  5. Parsed and validated content is passed to the “Content Seen?” component.
  6. “Content Seen?” checks if the HTML page is already in storage. If so, it’s discarded; otherwise, it’s passed to the Link Extractor.
  7. Link Extractor extracts links from the HTML pages.
  8. Extracted links are passed to the URL Filter.
  9. Filtered links are passed to the “URL Seen?” component.
  10. “URL Seen?” checks if a URL has been processed before. If yes, no action is taken.
  11. If a URL has not been processed before, it is added to the URL Frontier.

Step 3: Design Deep Dive

Depth-first search (DFS) vs Breadth-first search (BFS): The web can be viewed as a directed graph. BFS is generally preferred over DFS for web crawling because DFS can go very deep into a single path, which is impractical for the vast web. Standard BFS (FIFO queue) has issues with politeness (flooding a single host) and URL prioritization.

(Figure 5: Example of internal links from the same host causing politeness issues)

URL Frontier: This component addresses politeness, URL prioritization, and freshness. It stores URLs to be downloaded.

  • Politeness: Avoids sending too many requests to the same host. Implemented by mapping hostnames to worker threads, each with a separate FIFO queue for that host. A delay is added between downloads from the same host.

    • Queue router: Ensures each queue contains URLs from a single host.
    • Mapping table: Maps hosts to queues.
    • FIFO queues (b1…bn): Each for a specific host.
    • Queue selector: Maps worker threads to queues.
    • Worker threads: Download pages one by one from assigned queues with delays.

    (Figure 6: Design for politeness management in URL Frontier)

  • Priority: Prioritizes URLs based on usefulness (e.g., PageRank, traffic, update frequency). A “Prioritizer” component handles this, assigning different priorities to queues, with higher priority queues selected more frequently.

    (Figure 7: Design for URL priority management)

  • Freshness: Periodically recrawls downloaded pages to maintain data freshness. Strategies include recrawling based on update history and prioritizing important pages for more frequent recrawls.

  • Storage for URL Frontier: A hybrid approach is used: most URLs are on disk for scalability, while in-memory buffers are used for enqueue/dequeue operations to reduce disk I/O latency.

    (Figure 8: URL frontier design with front and back queues)

HTML Downloader: Downloads web pages using HTTP. Key considerations include:

  • Robots.txt: Adheres to the Robots Exclusion Protocol, which specifies allowed pages for crawling. The robots.txt file is cached to avoid repeated downloads.
  • Performance Optimization:

    1. Distributed crawl: Distribute crawl jobs across multiple servers and threads, partitioning the URL space.

      (Figure 9: Example of a distributed crawl)

    2. Cache DNS Resolver: Maintain a DNS cache to reduce latency from synchronous DNS requests.

    3. Locality: Geographically distribute crawl servers closer to website hosts for faster download times.

    4. Short timeout: Set a maximum wait time for unresponsive web servers to avoid long delays.

Robustness: Strategies to improve system robustness:

  • Consistent hashing: Distributes load among downloaders, allowing dynamic addition/removal of servers.
  • Save crawl states and data: Persist crawl states for easy restart after failures.
  • Exception handling: Gracefully handle errors without crashing the system.
  • Data validation: Prevent system errors through rigorous data validation.

Extensibility: Designing for flexibility to support new content types by plugging in new modules.

(Figure 10: How to add new modules for extensibility)

Detect and Avoid Problematic Content:

  • Redundant content: Use hashes or checksums to detect duplicate pages (approx. 30% of web content).
  • Spider traps: Web pages causing infinite loops. Can be partially avoided by setting max URL length, but often requires manual identification and customized URL filters.
  • Data noise: Exclude low-value content like advertisements, code snippets, or spam URLs.

Step 4: Wrap Up (Additional Considerations)

Beyond the core design, other important considerations for a large-scale web crawler include:

  • Server-side rendering: To retrieve dynamically generated links (e.g., by JavaScript), perform server-side rendering before parsing pages.
  • Filter out unwanted pages: Implement anti-spam components to filter low-quality and spam pages.
  • Database replication and sharding: Enhance data layer availability, scalability, and reliability.
  • Horizontal scaling: Keep servers stateless for efficient scaling across hundreds or thousands of servers.
  • Availability, Consistency, and Reliability: Fundamental principles for any successful large system.
  • Analytics: Collect and analyze data for system fine-tuning and insights.