Design Web Crawler

What is Web Crawler?

A Web Crawler is a distributed system that systematically browses the web to discover and index web pages. It's similar to systems used by Google, Bing, or other search engines. The service provides URL discovery, content extraction, and web page indexing.

Distributed web crawling with politeness policies and massive scale is what makes systems like Web Crawler unique. By understanding Web Crawler, you can tackle interview questions for similar crawling systems, since the core design challenges—URL discovery, content extraction, politeness policies, and scalability—remain the same.

Functional Requirements

Core (Interview Focussed)

• URL Discovery: Discover new URLs from web pages and sitemaps.
• Content Extraction: Extract and parse content from web pages.
• Politeness Policies: Respect robots.txt and rate limiting.
• Duplicate Detection: Avoid crawling duplicate URLs.

Out of Scope

• User authentication and authorization
• Content analysis and indexing
• Search result ranking
• Real-time crawling
• Mobile app specific features

Non-Functional Requirements

Core (Interview Focussed)

• High throughput: Process millions of web pages per day.
• Scalability: Handle billions of URLs and web pages.
• Fault tolerance: Handle network failures and server errors.
• Politeness: Respect website policies and rate limits.

Out of Scope

• Data retention policies
• Compliance and privacy regulations

💡 Interview Tip: Focus on high throughput, scalability, and fault tolerance. Interviewers care most about URL discovery, content extraction, and politeness policies.

Core Entities

💡 Interview Tip: Focus on URL, Page, and Domain as they drive URL discovery, content extraction, and politeness policies.

Core APIs

URL Management

• POST /urls { url, priority } – Add a new URL to crawl
• GET /urls/{url_id} – Get URL details
• PUT /urls/{url_id}/status { status } – Update URL status
• GET /urls?domain=&status=&limit= – List URLs with filters

Crawling

• POST /crawl/start { url_id } – Start crawling a URL
• GET /crawl/status { job_id } – Get crawling job status
• POST /crawl/stop { job_id } – Stop crawling job
• GET /crawl/queue?limit= – Get crawling queue

Content

• GET /pages/{page_id} – Get page content
• GET /pages/{page_id}/links – Get page links
• GET /pages?domain=&limit= – List pages with filters
• POST /pages/{page_id}/extract – Extract page content

Domain Management

• GET /domains/{domain_id} – Get domain details
• GET /domains/{domain_id}/robots – Get robots.txt content
• PUT /domains/{domain_id}/delay { delay } – Update crawl delay
• GET /domains?status=&limit= – List domains

High-Level Design

Key Components

• URL Discovery Service: Discover new URLs from web pages
• Content Extraction Service: Extract and parse web page content
• Crawling Scheduler: Schedule and manage crawling jobs
• Politeness Manager: Enforce politeness policies and rate limiting
• Duplicate Detector: Detect and avoid duplicate URLs
• Database: Persistent storage for URLs, pages, and domains

Mapping Core Functional Requirements to Components

Detailed Design

URL Discovery Service

Purpose: Discover new URLs from web pages and sitemaps.

Key Design Decisions:

• Link Extraction: Extract links from HTML content
• URL Normalization: Normalize URLs to avoid duplicates
• URL Validation: Validate URLs before adding to queue
• Priority Assignment: Assign priorities to discovered URLs

Algorithm: URL discovery

1. Receive web page content
2. Parse HTML content:
   - Extract all <a> tags
   - Extract href attributes
   - Extract anchor text
3. For each discovered URL:
   - Normalize URL format
   - Validate URL syntax
   - Check if URL already exists
   - If new URL:
     - Add to URL queue
     - Assign priority based on:
       - Source page authority
       - URL depth
       - Domain reputation
4. Store discovered links
5. Update URL statistics

Content Extraction Service

Purpose: Extract and parse content from web pages.

Key Design Decisions:

• HTML Parsing: Parse HTML content efficiently
• Content Extraction: Extract text, titles, and metadata
• Link Extraction: Extract links for further crawling
• Content Validation: Validate extracted content

Algorithm: Content extraction

1. Receive web page HTML
2. Parse HTML content:
   - Extract title tag
   - Extract meta tags
   - Extract body content
   - Extract links
3. Clean and normalize content:
   - Remove HTML tags
   - Normalize whitespace
   - Extract text content
4. Extract metadata:
   - Page title
   - Meta description
   - Keywords
   - Language
5. Store extracted content
6. Trigger URL discovery

Politeness Manager

Purpose: Enforce politeness policies and rate limiting.

Key Design Decisions:

• Robots.txt Compliance: Respect robots.txt rules
• Rate Limiting: Implement crawl delays per domain
• Politeness Policies: Enforce politeness rules
• Error Handling: Handle politeness violations gracefully

Algorithm: Politeness enforcement

1. Check domain politeness rules:
   - Fetch robots.txt
   - Parse robots.txt rules
   - Check crawl delay
   - Check disallowed paths
2. Before crawling URL:
   - Check if URL is allowed
   - Check crawl delay
   - Check rate limits
3. If politeness rules violated:
   - Delay crawling
   - Log violation
   - Update domain reputation
4. If rules allow:
   - Proceed with crawling
   - Update last crawl time
5. Monitor politeness compliance

Crawling Scheduler

Purpose: Schedule and manage crawling jobs.

Key Design Decisions:

• Job Scheduling: Schedule crawling jobs based on priority
• Load Balancing: Distribute crawling load across workers
• Fault Tolerance: Handle crawling failures gracefully
• Resource Management: Manage crawling resources efficiently

Algorithm: Crawling job scheduling

1. Receive URL crawling request
2. Check crawling constraints:
   - Domain politeness rules
   - Rate limits
   - Resource availability
3. If constraints allow:
   - Create crawling job
   - Assign to available worker
   - Set job priority
4. Monitor job execution:
   - Track job progress
   - Handle job failures
   - Retry failed jobs
5. Update job status
6. Clean up completed jobs

Database Design

URLs Table

Indexes:

• idx_domain on (domain) - Domain-based queries
• idx_status on (status) - Status-based queries
• idx_priority on (priority) - Priority-based scheduling

Pages Table

Indexes:

• idx_url_id on (url_id) - URL pages
• idx_title on (title) - Title-based queries

Domains Table

Indexes:

• idx_domain on (domain) - Domain lookup
• idx_status on (status) - Status-based queries

Crawl Jobs Table

Indexes:

• idx_status on (status) - Job status queries
• idx_priority on (priority) - Priority-based scheduling
• idx_created_at on (created_at) - Time-based queries

Scalability Considerations

Horizontal Scaling

• URL Discovery Service: Scale horizontally with load balancers
• Content Extraction Service: Use consistent hashing for URL partitioning
• Crawling Scheduler: Scale crawling jobs with distributed systems
• Database: Shard URLs and pages by domain

Caching Strategy

• Redis: Cache crawling queue and job status
• Application Cache: Cache frequently accessed data
• Database Cache: Cache URL and domain data

Performance Optimization

• Connection Pooling: Efficient database connections
• Batch Processing: Batch URL processing for efficiency
• Async Processing: Non-blocking crawling operations
• Resource Monitoring: Monitor CPU, memory, and network usage

Monitoring and Observability

Key Metrics

• Crawling Rate: URLs crawled per second
• Content Extraction Time: Average time to extract content
• Politeness Compliance: Percentage of politeness rule violations
• System Health: CPU, memory, and disk usage

Alerting

• High Latency: Alert when crawling time exceeds threshold
• Politeness Violations: Alert when politeness rules are violated
• Crawling Failures: Alert when crawling jobs fail
• System Errors: Alert on content extraction failures

Trade-offs and Considerations

Consistency vs. Availability

• Choice: Eventual consistency for URL discovery, strong consistency for crawling jobs
• Reasoning: URL discovery can tolerate slight delays, crawling jobs need immediate accuracy

Throughput vs. Politeness

• Choice: Balance crawling speed with politeness compliance
• Reasoning: Respect website policies while maintaining efficient crawling

Storage vs. Performance

• Choice: Use efficient storage for large-scale content
• Reasoning: Balance between storage costs and query performance

Common Interview Questions

Q: How would you handle robots.txt compliance?

A: Use robots.txt parsing, rule enforcement, and politeness monitoring to ensure robots.txt compliance.

Q: How do you avoid crawling duplicate URLs?

A: Use URL normalization, duplicate detection, and efficient data structures to avoid crawling duplicates.

Q: How would you scale this system globally?

A: Deploy regional crawling servers, use geo-distributed databases, and implement data replication strategies.

Q: How do you handle crawling failures?

A: Use retry mechanisms, error handling, and fault tolerance to handle crawling failures gracefully.

Key Takeaways

1. URL Discovery: Link extraction and URL normalization are essential for web crawling
2. Content Extraction: HTML parsing and content extraction enable web page indexing
3. Politeness Policies: Robots.txt compliance and rate limiting ensure respectful crawling
4. Scalability: Horizontal scaling and partitioning are crucial for handling large-scale crawling
5. Monitoring: Comprehensive monitoring ensures system reliability and performance