This blog post dives into the design of a serverless web crawler built on AWS, inspired by a Be A Better Dev video tutorial. The crawler's architecture utilizes AWS services like Lambda functions, SQS queues, and DynamoDB tables to efficiently crawl websites and discover connected URLs.
Problem and Motivation:
The author wanted to build a tool to analyze website connectivity, similar to a website crawling service they subscribed to. This tool would reveal all the URLs within a specific domain, providing valuable insights into the website's structure and external links.
Proposed Architecture:
The architecture involves two primary Lambda functions:
Initiation Lambda: This function triggers the crawl by taking a root URL as input. It generates a unique run ID and saves the initial URL and other crawl details in a DynamoDB table called "visited_urls." It then enqueues the URL into an SQS queue named "pending_crawl."
Processing Lambda: This function continuously polls the "pending_crawl" queue for URLs. Upon receiving a URL, it performs the following actions:
Visit the URL: It fetches the webpage content using an HTTP library.
Extract Links: It parses the HTML content to extract all linked URLs.
Deduplication: It checks the "visited_urls" table to avoid re-crawling already visited URLs.
Save Visited URLs: It saves the current URL and its referring URL (if any) in the "visited_urls" table.
Enqueue New URLs: It enqueues any unvisited URLs extracted from the current page back into the "pending_crawl" queue for further processing.
Challenges and Improvements:
The author identified several challenges and potential improvements for the architecture:
Run Completion Detection: Determining when a crawl is complete can be tricky, especially for large websites. The author suggests using a CloudWatch event to periodically check the queue size or implementing a more sophisticated approach like a Step Function workflow.
Concurrency Management: Uncontrolled concurrency can overload the target website. Implementing user-specific queues or dynamic code execution within a Step Function can help manage concurrent crawls effectively.
Visualization and Monitoring: A dashboard to visualize crawl progress and discovered URLs would be beneficial for managing multiple crawls and understanding website connectivity.
Overall, this blog post provides a detailed explanation of a serverless web crawler architecture on AWS. It highlights the key components, workflow, and potential challenges, making it a valuable resource for anyone interested in building similar crawlers or understanding the intricacies of serverless web scraping.
Additional Notes:
The video tutorial mentioned in the blog post is a great resource for a hands-on implementation of this architecture.
The blog post briefly touches on the potential impact of crawling large websites and the importance of responsible crawling practices.
I hope this blog post is helpful! Feel free to ask any further questions you may have about the serverless web crawler architecture or its implementation on AWS.
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