Design a web crawler in HLD - System Design Guide

Problem Statement

When a system tries to gather information from the web, it can easily get overwhelmed by the huge number of pages and links. Without a structured way to visit and collect data, it may miss important pages, revisit the same pages repeatedly, or overload websites with too many requests at once.

Solution

A web crawler systematically visits web pages by starting from a set of seed URLs, fetching their content, extracting links, and adding new links to a queue for future visits. It uses a scheduler to manage the order of URLs, respects website rules to avoid overloading, and stores visited URLs to prevent repeated crawling.

Architecture

Seed URLs

→URL Scheduler

↓

↓

Visited URL Store

↓

URL Frontier

URL Frontier

This diagram shows the flow of URLs starting from seed URLs, moving through scheduling, fetching, parsing, and storing visited URLs, with extracted links feeding back into the URL frontier for continuous crawling.

Trade-offs

✓ Pros

→

Efficiently manages large-scale web crawling by scheduling and queueing URLs.

→

Prevents repeated visits to the same page by tracking visited URLs.

→

Respects website load by controlling request rates and obeying robots.txt rules.

→

Modular design allows easy scaling and maintenance.

✗ Cons

→

Requires significant storage and memory to track visited URLs and manage queues at scale.

→

Complexity increases with handling dynamic content and JavaScript-heavy sites.

→

Latency can increase due to politeness delays and network variability.

When needing to crawl millions of web pages regularly with respect to website policies and when data freshness and coverage are important.

For small-scale or one-time data collection where a simple script suffices and overhead of a full crawler is unnecessary.

Real World Examples

Google

Google uses a distributed web crawler to index billions of web pages efficiently while respecting site rules and managing huge URL queues.

Bing

Bing's crawler prioritizes URLs based on freshness and importance to keep its search index updated and relevant.

Amazon

Amazon uses web crawlers to monitor competitor pricing and product availability by systematically visiting e-commerce sites.

Alternatives

Focused Crawler

Crawls only pages related to specific topics or keywords instead of the entire web.

Use when: When you need targeted data collection and want to reduce resource usage by ignoring irrelevant pages.

Incremental Crawler

Crawls only pages that have changed since the last crawl to save bandwidth and processing.

Use when: When data freshness is critical and full recrawling is too costly.

Deep Web Crawler

Designed to access content behind forms or requiring interaction, unlike standard crawlers that follow links.

Use when: When you need to extract data from dynamic or hidden web content.

Summary

A web crawler systematically visits web pages by managing URLs through scheduling, fetching, parsing, and storing.

It prevents overload and repeated visits by tracking visited URLs and respecting website rules.

This design supports large-scale, efficient, and polite data collection from the web.

Practice

1. What is the primary role of a web crawler in system design?

easy

A. To manage user authentication on websites

B. To display web pages to users

C. To automatically visit and collect data from websites

D. To store user preferences for websites

Solution

1. Step 1: Understand the function of a web crawler

   A web crawler is designed to visit websites automatically and collect data for indexing or analysis.

2. Step 2: Differentiate from other web functions

   Displaying pages, managing authentication, or storing preferences are not tasks of a crawler but of browsers or web servers.

3. Final Answer:

   To automatically visit and collect data from websites -> Option C

4. Quick Check:

   Web crawler = data collection [OK]

Hint: Crawler means automatic website data collection [OK]

Common Mistakes:

• Confusing crawler with browser functionality
• Thinking crawler manages user data
• Mixing crawler with server-side tasks

2. Which component is essential for managing the list of URLs to visit in a web crawler?

easy

A. User Interface

B. URL Frontier

C. Data Storage

D. HTML Parser

Solution

1. Step 1: Identify the URL management part

   The URL Frontier is the component that keeps track of URLs to be visited next in a crawler.

2. Step 2: Exclude unrelated components

   HTML Parser processes page content, Data Storage saves data, and User Interface is unrelated to URL management.

3. Final Answer:

   URL Frontier -> Option B

4. Quick Check:

   URL list manager = URL Frontier [OK]

Hint: URL list is managed by URL Frontier [OK]

Common Mistakes:

• Confusing parser with URL manager
• Thinking storage manages URLs
• Assuming UI handles crawling logic

3. Consider a crawler fetching pages with a politeness delay of 2 seconds per domain. If it visits 5 domains concurrently, how many pages can it fetch in 10 seconds?

medium

A. 50 pages

B. 10 pages

C. 5 pages

D. 25 pages

Solution

1. Step 1: Calculate pages per domain in 10 seconds

   With 2 seconds delay, each domain can be fetched 10 / 2 = 5 times in 10 seconds.

2. Step 2: Multiply by number of domains

   5 domains * 5 pages each = 25 pages total.

3. Final Answer:

   25 pages -> Option D

4. Quick Check:

   5 domains * 5 pages = 25 [OK]

Hint: Pages = (time/delay) * domains [OK]

Common Mistakes:

• Multiplying delay by domains incorrectly
• Ignoring concurrency in calculation
• Using total time as pages directly

4. A web crawler's URL Frontier is implemented as a simple queue. What problem might arise with this design?

medium

A. It may revisit the same URLs multiple times

B. It will fetch pages too quickly without delay

C. It cannot parse HTML content correctly

D. It will store data inefficiently

Solution

1. Step 1: Understand queue behavior in URL management

   A simple queue does not track visited URLs, so duplicates can be added and revisited.

2. Step 2: Identify consequences

   This causes repeated crawling of same pages, wasting resources.

3. Final Answer:

   It may revisit the same URLs multiple times -> Option A

4. Quick Check:

   Queue alone lacks duplicate check [OK]

Hint: Queue alone misses duplicate URL checks [OK]

Common Mistakes:

• Confusing queue with parser or storage issues
• Assuming queue controls fetch speed
• Thinking queue affects data storage

5. You want to design a scalable web crawler that respects website politeness and avoids overloading servers. Which approach best achieves this?

hard

A. Use distributed crawling with domain-based rate limiting and URL deduplication

B. Fetch all URLs as fast as possible without delay to maximize speed

C. Store all URLs in a single server queue without concurrency control

D. Ignore robots.txt and crawl all pages aggressively

Solution

1. Step 1: Identify scalability and politeness needs

   Distributed crawling allows scaling; domain-based rate limiting ensures politeness; URL deduplication prevents repeated visits.

2. Step 2: Evaluate other options

   Fetching fast without delay overloads servers; single queue limits scalability; ignoring robots.txt is unethical and risky.

3. Final Answer:

   Use distributed crawling with domain-based rate limiting and URL deduplication -> Option A

4. Quick Check:

   Distributed + rate limit + deduplication = scalable polite crawler [OK]

Hint: Combine distribution, rate limits, and deduplication [OK]

Common Mistakes:

• Ignoring politeness rules
• Centralizing all URLs causing bottlenecks
• Disregarding robots.txt rules