Book Summaries | System Design Interview (Part 1) - Design a Web Crawler

March 6th, 2024

Chapter 9: Design a Web Crawler

This summary will serve to further cement my learnings taken when reading the ninth chapter of System Design Interview - An Insider's Guide (Part 1), titled Design a Web Crawler, and I hope will provide some learnings to you as well.

Problem Understanding and Design Scope

The web crawler’s basic algorithm involves downloading web pages, extracting URLs, and updating the list. However, the challenge lies in designing a massively scalable web crawler within the interview duration. Questions posed to understand requirements and establish design scope include:

  • Main Purpose: Clarifying whether the crawler is for search engine indexing, data mining, or another purpose.
  • Monthly Web Pages: Establishing the scale by inquiring about the number of web pages collected per month, which, in this case, is 1 billion pages.
  • Content Types: Determining the content types, such as HTML only or including other formats like PDFs and images.
  • Newly Added or Edited Pages: Confirming if consideration should be given to newly added or edited web pages.
  • Storage Duration: Addressing how long HTML pages crawled from the web should be stored, with this example assuming up to 5 years.
  • Handling Duplicates: Ensuring clarity on how to handle web pages with duplicate content, with the instruction to ignore such pages.

High-Level Design and Buy-In

Following a clear understanding of the problem and scope, the text proposes a high-level design for the web crawler, seeking buy-in from stakeholders. The design is inspired by previous studies on web crawling. Key components include:

  • Seed URLs: Identified as the starting point for the crawl, seed URLs are crucial for traversing links effectively. Creative strategies for seed URL selection are proposed, considering factors like locality or topics.

  • URL Frontier: Split into to-be-downloaded and already-downloaded, this component manages the URLs efficiently, resembling a First-in-First-out (FIFO) queue.

  • HTML Downloader: Responsible for fetching web pages using the HTTP protocol, the HTML Downloader is introduced with considerations for performance optimization, including distributed crawl and cache DNS Resolver.

  • Components like DNS Resolver, Content Parser, Content Seen?, Content Storage, URL Extractor, URL Filter, URL Seen?, and URL Storage: These components together form the intricate workflow of the web crawler, ensuring the effective downloading, parsing, and storage of web pages.

Web Crawler Workflow

The web crawler workflow involves a systematic process with distinct steps, ensuring the effective collection and processing of web pages. The sequence is outlined as follows:

  1. Add seed URLs to the URL Frontier:

    • Seed URLs, strategically selected, serve as the starting point for the crawl.

  2. HTML Downloader fetches URLs:

    • The HTML Downloader retrieves a list of URLs from the URL Frontier, initiating the downloading process.

  3. Download and DNS Resolution:

    • HTML Downloader, with the assistance of DNS Resolver, translates URLs into IP addresses and downloads web pages.

  4. Content Parsing:

    • Content Parser validates and parses downloaded web pages to identify any malformed content that could pose issues.

  5. Content Seen? Component:

    • The “Content Seen?” component checks if the HTML page is already in storage, avoiding redundancy.

  6. Link Extraction:

    • If not in storage, the content is passed to the Link Extractor, extracting links from HTML pages.

  7. URL Filter:

    • Extracted links undergo filtering based on content types, file extensions, error links, and “blacklisted” sites.

  8. URL Seen? Component:

    • The “URL Seen?” component checks if a URL is already in storage, avoiding unnecessary processing.

  9. Add New URLs to Frontier:

    • Unprocessed URLs are added to the URL Frontier for subsequent crawling.

Design Deep Dive

Delving into the design specifics, several critical components and techniques are examined in-depth to understand their roles and optimizations:

  1. DFS vs. BFS:

    • The choice between Depth-First Search (DFS) and Breadth-First Search (BFS) for traversing the web graph is crucial. BFS, implemented as a First-In-First-Out (FIFO) queue, is favored over DFS due to its suitability for web crawlers.

  2. URL Frontier:

    • The URL Frontier is a pivotal data structure that manages URLs to be downloaded. It tackles challenges like politeness, URL prioritization, and ensuring freshness in the crawling process.

  3. HTML Downloader:

    • The HTML Downloader is responsible for fetching web pages using the HTTP protocol. Key considerations include the implementation of the Robots Exclusion Protocol (robots.txt) for crawler communication with websites.

  4. Performance Optimization:

    • To enhance performance, several optimizations are implemented, including distributed crawling, caching DNS Resolver results, geographical distribution for locality, and setting short timeouts to handle slow or unresponsive servers.

  5. Robustness Measures:

    • Robustness is addressed through consistent hashing for load distribution, saving crawl states and data to handle failures, effective exception handling, and data validation to prevent system errors.

  6. Extensibility:

    • The system’s extensibility is emphasized by illustrating how new modules can be seamlessly added, such as incorporating a PNG Downloader module or a Web Monitor module for copyright infringement prevention.

  7. Detect and Avoid Problematic Content:

    • Strategies for identifying and preventing problematic content are discussed, covering redundancy detection through hashes, steering clear of spider traps, and filtering out low-value content like advertisements and spam.

Storage for URL Frontier

Efficient management of the URL Frontier is crucial for the overall performance and scalability of the web crawler. Considering the potentially vast number of URLs involved, a thoughtful approach to storage is essential:

  1. Hybrid Approach:

    • Recognizing the challenges of handling hundreds of millions of URLs, a hybrid storage approach is adopted. The majority of URLs are stored on disk, leveraging the ample storage space provided by disk storage.

  2. In-Memory Buffers:

    • To balance the speed of access, buffers are maintained in memory to facilitate enqueueing and dequeueing operations. This mitigates the performance impact associated with reading from and writing to disk, providing a responsive and efficient system.

  3. Periodic Disk Writes:

    • To address the durability and scalability concerns associated with disk storage, data in the in-memory buffers are periodically written to disk. This strategic approach optimizes both storage space and access speed.

HTML Downloader

The HTML Downloader plays a pivotal role in acquiring web pages from the internet, using the HTTP protocol as its primary means. Several key aspects contribute to the effective functioning of the HTML Downloader:

  1. Robots Exclusion Protocol (Robots.txt):

    • Before initiating the crawl of a website, the crawler adheres to the Robots Exclusion Protocol, commonly known as robots.txt. This standard dictates which pages the crawler is allowed to download. Notably, the crawler caches the results of the robots.txt file to avoid repetitive downloads.

  2. Performance Optimization:

    • To achieve high performance, the crawl process is distributed across multiple servers, each running multiple threads. This distributed approach ensures efficient handling of a partitioned URL space, enhancing overall download speed.

  3. Cache DNS Resolver:

    • Recognizing that DNS requests can introduce latency, the HTML Downloader employs a cache for DNS Resolver results. This cache minimizes the time spent on DNS requests, enhancing the overall speed of the crawling process.

  4. Locality and Short Timeout:

    • Geographic distribution of crawl servers optimizes download time by reducing latency. Additionally, imposing short timeouts prevents prolonged waits for unresponsive servers, ensuring the timely progression of the crawl.

  5. Robustness Measures:

    • Consistent hashing is employed to distribute loads among downloaders, allowing for the seamless addition or removal of downloader servers. Consistent hashing contributes to improved system robustness, enhancing the overall reliability of the crawler.

  6. Data Validation:

    • Robustness is further fortified through diligent data validation. The HTML Downloader implements measures to handle errors gracefully, preventing system crashes in the face of common large-scale system challenges.

Robustness

Ensuring the robustness of the web crawler is paramount for its sustained and effective operation in the dynamic and unpredictable environment of the internet. Several strategies and components contribute to bolstering the robustness of the crawler:

  1. Consistent Hashing:

    • The implementation of consistent hashing aids in load distribution among downloaders. This dynamic approach facilitates the seamless addition or removal of downloader servers, contributing to the overall stability of the system.

  2. Save Crawl States and Data:

    • To safeguard against failures and disruptions, the crawler adopts a proactive approach by regularly saving crawl states and data to a storage system. In the event of a disruption, the system can be easily restarted by reloading these saved states and data, minimizing downtime.

  3. Exception Handling:

    • Acknowledging the inevitability of errors in large-scale systems, the crawler incorporates robust exception handling mechanisms. This ensures that errors are managed gracefully, preventing system crashes and allowing for the continuous execution of the crawling process.

  4. Data Validation:

    • An essential measure for preventing system errors, data validation is meticulously implemented. Validating data at various stages of the crawling process mitigates the risk of corrupted or erroneous data affecting the overall integrity and functionality of the system.

Extensibility

An adaptable and extensible design is fundamental for the long-term viability and versatility of the web crawler. The extensibility of the system is achieved through modularization and the ability to seamlessly integrate new functionalities. Key aspects of the extensibility framework include:

  1. Modular Design:

    • The crawler employs a modular architecture that allows for the integration of new components or modules with minimal disruption to the existing system. This modularization facilitates the addition of new features, making the system flexible and responsive to evolving requirements.

  2. Dynamic Module Integration:

    • New modules can be effortlessly plugged into the existing framework to enhance or extend the crawler’s capabilities. For instance, introducing a “PNG Downloader” module or a “Web Monitor” module involves simply integrating these modules into the existing architecture, showcasing the system’s adaptability.

  3. Prioritization Mechanism:

    • The prioritization component, known as the “Prioritizer,” offers a mechanism for dynamically assigning priorities to URLs based on various factors such as PageRank, web traffic, or update frequency. This feature allows for the customization and expansion of prioritization criteria as needed.

  4. Scalability for New Content Types:

    • The system is designed to accommodate new content types seamlessly. For example, the addition of a “PNG Downloader” module demonstrates how the crawler can be extended to support the downloading of specific file types without necessitating a complete overhaul of the existing infrastructure.

Detect and Avoid Problematic Content

To enhance the robustness and efficiency of the web crawler, a comprehensive strategy for identifying and mitigating problematic content is crucial. The section on detecting and avoiding problematic content encompasses various aspects aimed at ensuring the quality and reliability of the crawled data:

  1. Redundant Content Detection:

    • Approximately 30% of web pages are duplicates, emphasizing the need for a robust redundancy detection mechanism. The crawler employs hash values or checksums to identify redundant content efficiently, mitigating the storage of duplicate information.

  2. Spider Traps Mitigation:

    • Spider traps, which lead crawlers into infinite loops, pose a potential threat. The system combats this by implementing a maximal length for URLs, preventing the crawler from getting stuck in lengthy and repetitive directory structures. Although automatic identification of spider traps remains challenging, manual verification and exclusion measures can be applied.

  3. Data Noise Exclusion:

    • To maintain the quality of crawled data, the system filters out irrelevant or low-value content such as advertisements, code snippets, and spam URLs. This proactive approach ensures that the collected data remains focused on meaningful and valuable information.