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Chapter 9: Design a Web Crawler

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What is a web crawler?

A web crawler (also known as a robot or spider) is used by search engines to discover new or updated content on the web.

The basic algorithm is:

  1. Given a set of URLs, download the web pages.

  2. Extract URLs from these pages.

  3. Add new URLs to the list to be downloaded and repeat.

Characteristics of web crawlers:

  • Scalability: Must be extremely efficient using parallelization to handle the web's massive size.

  • Robustness: Must handle web traps like bad HTML, unresponsive servers, and malicious links.

  • Politeness: Should not make too many requests to a website within a short time interval.

  • Extensibility: Should be flexible so minimal changes are needed to support new content types.

Establish Design Scope​

This phase clarifies the system's requirements and constraints. The main points derived from the Q&A with the interviewer are:

Key Requirements

  • Purpose: Search engine indexing.

  • Scale: Crawl 1 billion web pages per month.

  • Content Type: HTML only.

  • Freshness: The crawler must consider newly added or edited web pages.

  • Storage: Store crawled HTML pages for up to 5 years.

  • Deduplication: Pages with duplicate content should be ignored.

Back-of-the-envelope Estimation

The main points derived from the Q&A with the interviewer are:

  • QPS (Queries Per Second)

1,000,000,000 pages / 30 days / 24 hours / 3600 seconds ≈ 400 QPS

Peak QPS is estimated at 2 * QPS = 800 QPS.

  • Storage

1 billion pages * 500k/page = 500 TB per month. (Assuming an average page size of 500k)

Total storage for 5 years: 500 TB/month * 12 months * 5 years = 30 PB.

High-Level Architecture & Workflow​

High-Level Architecture​

This is a breakdown of the main building blocks of the web crawler.

High-Level Architecture

  • Seed URLs: The initial set of URLs that the crawler uses as a starting point.

  • URL Frontier: Stores the URLs that are yet to be downloaded. Acts as the manager for the crawler's "to-do list".

  • HTML Downloader: Fetches the raw HTML content of a web page from the internet.

  • DNS Resolver: Translates a website's hostname (e.g., en.wikipedia.org) into an IP address.

  • Content Parser: Parses the downloaded HTML to extract its structure and ensure it's not malformed.

  • Content Seen?: Checks if the exact same content has been seen before (from a different URL) to prevent storing duplicates. Usually works by comparing hashes.

  • Content Storage: The storage system (e.g., a database or a distributed file system) that holds the content of downloaded pages.

  • URL Extractor: Extracts all hyperlink URLs from a downloaded HTML page.

  • URL Filter: Filters out unwanted or irrelevant links based on predefined rules (e.g., file extensions, blacklisted sites).

  • URL Seen?: A data structure (like a Bloom filter or hash table) that tracks if a URL has already been processed or is currently in the frontier to prevent redundant work and infinite loops.

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

Workflow​

This is the step-by-step process showing how a URL is processed through the system.

High-Level Architecture

  1. Start: Add seed URLs to the URL Frontier.

  2. Fetch: The HTML Downloader gets a list of URLs from the URL Frontier.

  3. Download: The downloader gets the IP address from the DNS Resolver and downloads the page.

  4. Parse: The downloaded page is sent to the Content Parser.

  5. Check Content Duplicates: The parsed content is checked by "Content Seen?".

  6. Discard or Process: If the content is a duplicate, it's discarded. If it's new, it's sent to the URL Extractor.

  7. Extract Links: The URL Extractor pulls all links from the page content.

  8. Filter Links: The extracted links are passed to the URL Filter.

  9. Check URL Duplicates: The filtered links are checked by "URL Seen?".

  10. Discard or Add: If a URL has already been visited or is in the frontier, it's discarded.

  11. Add to Frontier: If the URL is new, it's added to the URL Frontier for future crawling. The cycle repeats.

Deep Dive into Key Components and Challenges​

DFS vs. BFS​

You can think of the web as a massive, directed graph. Crawling is essentially a graph traversal problem.

  • DFS (Depth-First Search): Generally not a good choice. A crawler using DFS could easily get stuck in a very deep "rabbit hole" within a single website (e.g., domain.com/a/b/c/...).

  • BFS (Breadth-First Search): Commonly used by web crawlers. It explores the web layer by layer. The URL Frontier is essentially a large-scale, sophisticated implementation of a BFS queue.

Crawler Traversal Strategy

The URL Frontier​

URL Frontier

A simple FIFO queue for BFS is not enough. A well-designed URL Frontier must handle:

Politeness​

  • Problem: A single queue may batch many URLs from the same host, causing bursty, impolite traffic.
  • Design (Back queues model):
    • Create multiple queues, one per host.
    • A "Queue Router" assigns each URL to its host-specific queue.
    • Worker threads consume from one host at a time with a configurable delay between requests.

Priority​

  • Problem: Not all pages are equally important; FIFO has no notion of importance.
  • Design (Front queues model):
    • A Prioritizer assigns a score (e.g., PageRank, traffic).
    • Maintain multiple queues (f1, f2, ..., fn) by priority level.
    • A "Queue Selector" favors higher-priority queues when pulling work.

Freshness​

  • Problem: Web pages change frequently; recrawling everything uniformly is too slow and expensive.
  • Design:
    • Re-crawl pages based on estimated update frequency.
    • Prioritize important pages to be re-crawled more often.

Storage​

  • Problem: The frontier can contain hundreds of millions of URLs; RAM is insufficient and disk I/O can bottleneck.
  • Design (Hybrid approach):
    • Keep the majority of URLs on disk.
    • Use fast in-memory buffers for enqueue/dequeue, flushing to disk periodically.

The HTML Downloader​

The HTML Downloader is responsible for the actual network requests to fetch web pages. Its design involves a balance between performance and respecting website rules.

Robots Exclusion Protocol (robots.txt)​

  • What it is: robots.txt is a standard file that websites use to communicate with crawlers. It specifies which parts of the site crawlers are allowed or disallowed to access.
  • Rule: A polite crawler must check a site's robots.txt file before crawling it and strictly follow its rules.
  • Optimization: Cache robots.txt per host and refresh it periodically to avoid fetching on every request.

Performance and Reliability​

  • Distributed crawl: Distribute crawl jobs across multiple servers; each handles a subset of the URL space (horizontal scaling).

Distributed Crawl

  • Cache DNS resolver: Maintain a local DNS cache to avoid the 10–200ms lookup penalty on every request.
  • Locality: Place crawl servers near target regions to reduce network latency and speed up downloads.
  • Short timeout: Enforce a maximum wait time; if a server does not respond within the timeout, abandon the request and move on.

Robustness​

A large-scale distributed system must be designed to be resilient to failures.

Techniques​

  • Consistent hashing: Distribute load (e.g., URLs) evenly across downloader servers to simplify scale out/in without massive rebalancing.
  • Checkpoint crawl state: Periodically persist system state (e.g., URL Frontier) so a disrupted crawl can resume from the last checkpoint.
  • Graceful exception handling: Handle frequent errors (network failures, malformed HTML) without crashing the system.
  • Data validation: Validate inputs and intermediate data to prevent propagation of malformed data.

Extensibility​

The system should be flexible enough to support new content types and features in the future without a complete redesign.

Crawler Extensibility

Design Approach​

  • Pluggable modules: Add capabilities without redesign (e.g., a PNG Downloader, or a Web Monitor for copyright checks).

Detect and Avoid Problematic Content​

The web is messy. The crawler must be able to handle redundant, meaningless, or harmful content.

Problems​

  • Duplicate content: Nearly 30% of web content is duplicated.
  • Spider traps: Servers can generate infinite paths (e.g., .../foo/bar/foo/bar/...).
  • Low-value noise: Ads, code snippets, or spam provide little value.

Mitigations​

  • Hash/checksum-based deduplication to detect and discard duplicates.
  • Set a maximum URL length to avoid traps; ignore URLs exceeding the limit.
  • Filter and exclude low-value content where possible.

Further Considerations​

This final section covers advanced topics and other important aspects for building a production-ready web crawler.

Server-side Rendering (SSR)​

  • Problem: Many modern websites generate links dynamically with JavaScript or AJAX, which a simple HTML downloader cannot discover.
  • Solution: Perform server-side or dynamic rendering before parsing to execute page scripts and obtain the final HTML with all generated links.

Filter Unwanted Pages​

  • Rationale: Storage and crawl budget are finite; avoid low-quality content.
  • Approach: Use an anti-spam/quality component to identify and filter spam or low-value pages so resources focus on meaningful content.

Data Layer: Replication and Sharding​

  • Replication: Create database copies to improve availability and read scalability.
  • Sharding: Partition data across databases to scale writes and storage.
  • Goal: Increase availability, scalability, and reliability of the data layer.

Horizontal Scaling​

  • Run on hundreds or thousands of servers.
  • Design crawling servers to be stateless to enable elastic scaling and simplified operations.

Availability, Consistency, and Reliability​

  • Core concerns for any large system; evaluate trade-offs and apply them across components.

Analytics​

  • Collect and analyze telemetry to monitor crawler health and performance.
  • Use insights from analytics to tune system behavior and improve efficiency.

Mid-Level Engineer Interview Questions​

Why do web crawlers typically use Breadth-First Search (BFS) over Depth-First Search (DFS)?

Mid-Level

Can you explain the main responsibilities of a URL Frontier? How does it differ from a simple queue?

Mid-Level

If you were asked to design a 'polite' crawler, how would you implement it to avoid making requests to the same website too frequently?

Mid-Level

Why can DNS resolution become a bottleneck for a web crawler? How would you optimize it?

Mid-Level

We need to avoid crawling the same URL twice and also avoid storing identical page content that comes from different URLs. How would you handle these two different kinds of duplication?

Mid-Level

What is robots.txt? Why is it essential for a crawler to respect it?

Mid-Level

What should a Crawler Agent do when it tries to download a URL, but the target server is unresponsive for a long time?

Mid-Level

When designing the 'URL Seen?' component, what data structures would you consider? What are their respective pros and cons?

Mid-Level

Please describe the complete lifecycle of a URL, from the moment it is discovered until its page content is stored.

Mid-Level

What is a spider trap? Please give an example and explain how to avoid it.

Mid-Level

Senior Engineer Interview Questions​

What are the pros and cons of designing page parsing as a separate microservice versus embedding the logic within the Crawler Agent? Which would you choose for a large-scale system and why?

Senior

Our current system handles 1 billion pages/month. The new goal is 100 billion/month. What new bottlenecks would you anticipate, and how would you evolve the architecture for 100x scale?

Senior

Discuss storage options for the URL Frontier. What are the trade-offs between in-memory, disk-based, or hybrid? How would you ensure data consistency under high concurrency?

Senior

How do we ensure the freshness of crawled data? Design a strategy to decide when and how often to re-crawl a page.

Senior

If the master node of the URL Frontier or the entire service goes down, what is your disaster recovery plan to minimize data loss and downtime?

Senior

Major sites deploy anti-crawling mechanisms. If our crawler is being blocked, what multi-faceted strategy would you develop?

Senior

Estimate monthly operational costs (bandwidth, compute, storage) to crawl 1 billion pages. State assumptions and identify the largest expense.

Senior

What key metrics would you monitor to measure system health, performance, and crawl quality?

Senior

If we need to start crawling PDFs and images, what design changes are required? How can the initial design make this easier?

Senior

A Crawler Agent leases a URL and crashes before completion. How do you ensure the URL is not lost and is eventually re-processed?

Senior
Last updated on by Philly Chien