Design a web crawler in HLD - Scalability & System Analysis

Scalability Analysis - Design a web crawler

Growth Table: Web Crawler Scaling

Users / Scale	100 URLs	10K URLs	1M URLs	100M URLs
Pages to Crawl	100	10,000	1,000,000	100,000,000
Crawler Instances	1	5-10	100-200	10,000+
Storage Needed	MBs	GBs	TBs	Petabytes
Database QPS	10-50	500-1000	10,000+	100,000+
Network Bandwidth	Low	Moderate	High (Gbps)	Very High (Multiple Gbps)
URL Frontier Size	Small	Medium	Large	Very Large (Distributed)

First Bottleneck

The first bottleneck is the URL frontier management and database. As the crawler scales, managing the queue of URLs to visit and storing crawl data grows rapidly. The database can become overwhelmed by high query rates for URL fetching, status updates, and storing page data.

Scaling Solutions

• Horizontal Scaling: Add more crawler instances to distribute crawling load.
• Distributed URL Frontier: Use distributed queues or message brokers to manage URLs efficiently.
• Database Sharding: Partition the database by URL hash or domain to reduce load on single instances.
• Caching: Cache DNS lookups and page content to reduce repeated network calls.
• Politeness and Rate Limiting: Respect site crawl limits to avoid overload and bans.
• Use CDN or Proxy Pools: To distribute network load and avoid IP blocking.
• Incremental Crawling: Prioritize fresh or changed pages to reduce unnecessary crawling.

Back-of-Envelope Cost Analysis

• At 1M URLs, assuming 1 request per page, 10 requests/sec sustained crawling rate.
• Storage: 1M pages * 100KB average = ~100GB storage needed.
• Bandwidth: 10 requests/sec * 100KB = ~1MB/sec (~8Mbps) network usage.
• Database QPS: 10,000+ queries per second for URL status updates and metadata.
• CPU: Multiple crawler instances needed to handle parsing and network IO.

Interview Tip

Start by defining the crawler's main components: URL frontier, fetchers, parsers, storage. Discuss bottlenecks at each scale and propose targeted solutions like sharding, caching, and horizontal scaling. Always mention politeness and real-world constraints like site limits and network bandwidth.

Self Check

Your database handles 1000 QPS. Traffic grows 10x. What do you do first?

Answer: Implement database sharding or add read replicas to distribute load and prevent the database from becoming a bottleneck.

Key Result

The URL frontier and database become the first bottlenecks as crawling scales; distributing URL management and sharding storage are key to scaling efficiently.

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