What is the design of a 10k machine web crawling system?

Designing a web crawling system for 10,000 machines is a complex task that requires a distributed and scalable architecture. Here’s a breakdown of the key components and considerations:

1. Distributed Crawlers:

- Architecture: Each of the 10,000 machines acts as an independent crawler, meaning each machine is responsible for fetching and processing web pages. This is to maximize the throughput and speed of web crawling. Each machine will have its own local storage and processing capabilities. - Tasks: Each crawler instance would maintain its own queue of URLs to crawl. URL lists would be divided amongst the machines using a hashing method to distribute the load evenly. It is important to ensure each node can independently process the URLs without requiring too much communication between the nodes.

2. URL Frontier (Queue):

- Sharding: The URL frontier must be sharded (divided) to avoid overloading a single queue. A distributed queue system, like Apache Kafka or RabbitMQ, can be used to distribute URLs to different crawler machines. - Priority: URLs should be prioritized based on various criteria such as PageRank, freshness, or site importance. This is to ensure that the most important pages get crawled first. A priority queue implementation might be needed.

3. DNS Resolution:

- Distributed DNS Resolver: With 10k machines, the volume of DNS lookups is very high. Use a distributed DNS resolver or a local caching DNS server on each node to minimize latency and handle DNS lookups efficiently.

4. Data Storage:

- Crawled Content Storage: The fetched web pages should be stored in a distributed storage system, like Hadoop Distributed File System (HDFS), Amazon S3, or Google Cloud Storage. - Database: Use a distributed database (like Cassandra or HBase) to store metadata about the crawled pages, such as timestamps, links, and other relevant information.

5. Parser & Extractor:

- HTML Parser: A robust HTML parser is required to extract text, links, and other structured information from the web pages. Libraries like Beautiful Soup or Jsoup are often used. - Link Extractor: A component must extract all the valid links from the fetched web pages. These links need to be added to the URL frontier for crawling in future steps.

6. Scheduler:

- Rate Limiting: Implement rate limiting to respect website’s `robots.txt` rules and to avoid overloading any single server. - Retry Mechanism: Implement a system for retrying failed fetches or connection errors to ensure resilience of the system. - Dynamic Scheduling: Use feedback from crawl performance to dynamically adjust the crawl rate and prioritize different URL groups.

7. Monitoring and Logging:

- Centralized Logging: Use a centralized logging system (like Elasticsearch, Logstash, and Kibana, also known as the ELK stack) to monitor the performance of the crawling system. This will ensure the overall system's health. - Alerting: Implement an alert system to notify administrators of errors, slow performance, or failures in the web crawling process.

8. Communication:

- Message Queues: Use message queues like RabbitMQ or Apache Kafka to communicate between different system components asynchronously. - RPC: Use Remote Procedure Call mechanisms, such as gRPC, if any service-to-service communication is necessary.

9. Fault Tolerance:

- Redundancy: Design the system with redundancy in all areas to handle machine failures. If a crawler machine fails, its assigned work should be reassigned to another available machine. - Replication: All databases and storage systems should use replication so that in the event of a failure the data isn't lost.

10. Security:

- Firewalls: Implement firewalls to protect the crawling infrastructure from external threats. - Authentication & Authorization: Secure communication between system components using appropriate security measures like TLS encryption and API keys.

In summary, a 10k machine web crawling system requires a well-architected, distributed, and fault-tolerant setup, with careful consideration of resource management, scalability, and resilience. Each component needs to work together efficiently to ensure the successful crawling of a large number of web pages.

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