Design a Web Crawler at Google Scale

Question

Design a web crawler that indexes billions of pages. Cover URL frontier and prioritization, politeness policies, distributed crawling coordination, content deduplication, and incremental re-crawling strategies.

Codemia · Accepted Answer

### Functional Requirements
1. **Web Page Fetching**: The crawler must be able to fetch HTML content from billions of URLs.
2. **URL Frontier Management**: Maintain a prioritized list of URLs to crawl based on factors such as relevance and freshness.
3. **Content Indexing**: Extract and index key content from fetched pages.
4. **Politeness Policy**: Implement rules to avoid overloading servers and respect robots.txt.
5. **Content Deduplication**: Identify and eliminate duplicate pages in the index.
6. **Incremental Crawling**: Re-crawl pages that have changed since the last fetch.

### Non-Functional Requirements
1. **Scalability**: The system should handle scaling to billions of pages.
2. **Fault Tolerance**: Ensure robustness against failures in any component.
3. **Latency**: Aim for low latency in fetching and indexing pages, ideally under 200ms per page.
4. **Data Consistency**: Ensure the index is consistent and up-to-date across distributed systems.

Assuming we need to crawl and index 1 billion pages:
- **Crawl Rate**: If we crawl 100,000 pages per minute, it would take approximately 10,000 minutes (~7 days) to initially crawl the entire web.
- **Data Storage**: Each page averages 1 MB; totaling around 1 TB for raw HTML content. Indexing and metadata could require an additional 4 TB, leading to a total of ~5 TB for initial storage.
- **Daily Updates**: Assuming 10% of pages change daily, this results in roughly 100 million pages to re-crawl daily, requiring an additional 100 GB of storage for changes and updates.

The architecture can be broken down into several components:
- **URL Frontier**: A distributed queue (e.g., using Google Cloud Pub/Sub) for managing URLs to be crawled. Prioritization logic can be implemented using a microservice architecture.
- **Crawler Workers**: A pool of distributed crawlers (Docker containers on Kubernetes) that fetch pages and send them to the indexing service.
- **Indexing Service**: Utilize Bigtable for storing the indexed content and metadata, optimized for quick read/writes.
- **Deduplication Service**: A dedicated service that checks for duplicates using a hash-based approach before content is indexed.
- **Politeness Manager**: A service that enforces crawling limits to respect robots.txt and server load.
- **Monitoring Dashboard**: Real-time monitoring using Google Cloud Operations to track crawler health and performance.

### Schema Design
1. **Page** Table:
   - `url`: STRING (Primary Key)
   - `content`: BYTES (HTML content)
   - `last_crawled`: TIMESTAMP
   - `hash`: STRING (for deduplication)
   - `status`: ENUM ('pending', 'crawled', 'error')

2. **Crawl Metadata** Table:
   - `url`: STRING (Foreign Key)
   - `crawl_attempts`: INT
   - `next_crawl`: TIMESTAMP
   - `politeness_delay`: INT (milliseconds)

### Access Patterns
- Frequent reads for the latest content and metadata.
- Frequent writes for newly crawled pages and updates.

1. **Distributed vs. Centralized URL Frontier**: A distributed URL frontier allows for greater scalability but increases complexity in maintaining order and priority across nodes.
2. **Crawling Speed vs. Politeness**: Increasing crawl speed can lead to server overload; we prioritize politeness to ensure sustainability and avoid IP banning.
3. **Storage vs. Processing**: Choosing between more storage for raw pages versus processing them to reduce size; we opt for a balance to keep recent and valuable data accessible.
4. **Real-time vs. Batch Processing**: Real-time indexing provides fresh content but requires more resources; we implement a hybrid approach to maintain efficiency.

Design a Web Crawler at Google Scale

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