Design a web crawler in HLD - Architecture Diagram

System Overview - Design a web crawler

A web crawler automatically browses the internet to collect and index web pages. It must efficiently fetch pages, avoid duplicate visits, and handle large-scale data with fault tolerance.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Scheduler ---> URL Frontier (Queue) ---> Fetcher Pool ---> Parser ---> Storage (Database)
                                         |                              |
                                         v                              v
                                      Cache                         Message Queue

Components

User

client

Initiates crawl requests and views crawl status

Load Balancer

load_balancer

Distributes incoming crawl requests evenly to API Gateway

API Gateway

api_gateway

Receives requests, handles authentication, and routes to Scheduler

Scheduler

service

Manages crawl jobs and schedules URLs to be fetched

URL Frontier (Queue)

queue

Stores URLs to be crawled in order and avoids duplicates

Fetcher Pool

service

Fetches web pages from URLs concurrently

Parser

service

Extracts links and content from fetched pages

Storage (Database)

database

Stores crawled page data and metadata

Cache

cache

Caches recently fetched pages to reduce duplicate fetches

Message Queue

queue

Handles asynchronous communication between Parser and Scheduler

Request Flow - 10 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Scheduler

Scheduler → URL Frontier (Queue)

Fetcher Pool → Cache

Fetcher Pool → Web Servers (Internet)

Fetcher Pool → Parser

Parser → Storage (Database)

Parser → Message Queue

Message Queue → Scheduler

Failure Scenario

Component Fails:Database

Impact:New page data cannot be stored; crawl continues but data loss occurs

Mitigation:Use database replication and failover; cache recent pages to reduce data loss

Architecture Quiz - 3 Questions

Which component ensures URLs are not crawled multiple times?

A API Gateway

B Fetcher Pool

C URL Frontier (Queue)

D Load Balancer

Design Principle

This design uses a modular pipeline with queues and caches to handle large-scale crawling efficiently. It separates concerns: scheduling, fetching, parsing, and storage, enabling scalability and fault tolerance.

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