What if you could explore the entire web automatically, without lifting a finger?
Why Design a web crawler in HLD? - Purpose & Use Cases
Start learning this pattern below
Jump into concepts and practice - no test required
Imagine you want to collect information from thousands of websites by visiting each page one by one manually.
You open a browser, type a URL, read the content, copy the data, then move to the next link.
This process is slow and exhausting, especially when websites have millions of pages.
Manually visiting pages is extremely slow and prone to mistakes like missing pages or copying wrong data.
It is impossible to keep up with constantly changing websites and huge volumes of data.
You also cannot easily organize or update the collected information without automation.
A web crawler automates visiting web pages, extracting data, and following links systematically.
It can work 24/7, handle millions of pages, and organize data efficiently.
This saves time, reduces errors, and scales to the size of the internet.
open browser visit url copy data find next link repeat
start crawler fetch page extract data enqueue links repeat automatically
It enables automatic, large-scale collection and updating of web data without human effort.
Search engines like Google use web crawlers to index billions of web pages so you can find information instantly.
Manual web data collection is slow and error-prone.
Web crawlers automate and scale this process efficiently.
This allows building powerful services like search engines and data analytics.
Practice
Solution
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.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.Final Answer:
To automatically visit and collect data from websites -> Option CQuick Check:
Web crawler = data collection [OK]
- Confusing crawler with browser functionality
- Thinking crawler manages user data
- Mixing crawler with server-side tasks
Solution
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.Step 2: Exclude unrelated components
HTML Parser processes page content, Data Storage saves data, and User Interface is unrelated to URL management.Final Answer:
URL Frontier -> Option BQuick Check:
URL list manager = URL Frontier [OK]
- Confusing parser with URL manager
- Thinking storage manages URLs
- Assuming UI handles crawling logic
Solution
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.Step 2: Multiply by number of domains
5 domains * 5 pages each = 25 pages total.Final Answer:
25 pages -> Option DQuick Check:
5 domains * 5 pages = 25 [OK]
- Multiplying delay by domains incorrectly
- Ignoring concurrency in calculation
- Using total time as pages directly
Solution
Step 1: Understand queue behavior in URL management
A simple queue does not track visited URLs, so duplicates can be added and revisited.Step 2: Identify consequences
This causes repeated crawling of same pages, wasting resources.Final Answer:
It may revisit the same URLs multiple times -> Option AQuick Check:
Queue alone lacks duplicate check [OK]
- Confusing queue with parser or storage issues
- Assuming queue controls fetch speed
- Thinking queue affects data storage
Solution
Step 1: Identify scalability and politeness needs
Distributed crawling allows scaling; domain-based rate limiting ensures politeness; URL deduplication prevents repeated visits.Step 2: Evaluate other options
Fetching fast without delay overloads servers; single queue limits scalability; ignoring robots.txt is unethical and risky.Final Answer:
Use distributed crawling with domain-based rate limiting and URL deduplication -> Option AQuick Check:
Distributed + rate limit + deduplication = scalable polite crawler [OK]
- Ignoring politeness rules
- Centralizing all URLs causing bottlenecks
- Disregarding robots.txt rules
