Design a Web Crawler

Question

Design a large-scale web crawler that can index billions of pages. Cover URL frontier management, politeness policies, deduplication, content extraction, and distributed crawling coordination.

Codemia · Accepted Answer

### Functional Requirements
1. **URL Frontier Management**: Maintain a priority queue of URLs to be crawled, ensuring efficient fetching of new content.
2. **Politeness Policies**: Implement rules to avoid overwhelming websites, such as respecting `robots.txt` and rate limiting requests.
3. **Deduplication**: Identify and filter duplicate content across different pages to ensure unique index entries.
4. **Content Extraction**: Extract relevant data from HTML documents, including titles, body text, and metadata for indexing.
5. **Distributed Crawling Coordination**: Support multiple crawler instances that can coordinate to avoid redundant crawling and share progress.

### Non-Functional Requirements
1. **Scalability**: Ability to scale to billions of pages with minimal performance degradation.
2. **Fault Tolerance**: Ensure the system can recover from failures without losing progress or data.
3. **Performance**: Aim for low latency in fetching and indexing content, with high throughput.
4. **Maintainability**: Design for easy updates and modifications without significant downtime.

Assuming we aim to crawl 1 billion pages:
- **Fetch Rate**: If each crawler can fetch 10 pages per second and we have 100 concurrent crawlers, the total fetch rate is 1,000 pages/second.
- **Crawl Time**: To crawl 1 billion pages, it would take approximately 1 billion / 1,000 = 1,000,000 seconds or about 11.5 days.
- **Storage Requirements**: Assuming an average webpage size of 2MB, total storage needed would be 2 billion MB or approximately 2 PB. We need to account for deduplication, so let's estimate 1.5 PB for actual unique content.
- **Network Bandwidth**: A total of 1,000 pages per second at 2MB each would require 2,000 MB/s or 2 Gbps of bandwidth.

### Component Diagram
- **Crawler Nodes**: Distributed nodes responsible for fetching pages. Technologies: Go or Python with asynchronous libraries for handling requests efficiently.
- **URL Frontier**: A distributed priority queue, using Apache Kafka for managing the URL queue.
- **Content Storage**: A NoSQL database like Apache Cassandra for storing fetched content, allowing for high write and read throughput.
- **Deduplication Service**: A microservice that checks for existing content using a hash function and stores unique entries.
- **Coordinator Service**: An orchestration layer using Kubernetes to manage the deployment and scaling of crawler nodes.
- **Monitoring & Logging**: Use Prometheus and Grafana for monitoring and ELK stack for logging.

### Schema Design
- **URL Frontier Table**:
  - `url` (string): URL to be crawled
  - `priority` (int): Crawling priority
  - `last_crawled` (timestamp): Last time the URL was crawled
- **Content Table**:
  - `url` (string, primary key): Unique URL
  - `title` (string): Title of the page
  - `body` (text): Main content extracted
  - `metadata` (JSON): Additional metadata like keywords, descriptions
  - `hash` (string): Unique hash of content for deduplication
- **Crawl History Table**:
  - `url` (string): URL of the crawled page
  - `crawl_timestamp` (timestamp): When it was crawled
  - `status` (string): Status of fetch (success, failed, etc.)

1. **Consistency vs. Availability**: Choosing a NoSQL database like Cassandra gives us high availability and partition tolerance but might introduce eventual consistency issues. In this context, eventual consistency is acceptable since real-time updates to the index are not critical.
2. **Politeness vs. Performance**: Implementing strict politeness policies can reduce throughput. We can balance this by allowing higher fetch rates for trusted domains and lowering rates for less reliable ones.
3. **Deduplication Complexity**: Deduplication adds overhead to the crawling process. We can mitigate this by using efficient hashing algorithms but must manage memory and processing costs associated with storing hashes.
4. **Single vs. Multiple Crawler Instances**: Using multiple instances improves speed but introduces complexity in coordination and data consistency. A robust coordination mechanism is essential to avoid race conditions.

Design a Web Crawler

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