How to Implement Web Scraping with N&N: A Guide to Data Storage Without Third-Party Platforms

Web scraping has become an essential tool for businesses looking to extract valuable information from websites without complex API integrations. This technique, often called data storage, allows you to gather product information, company details, and other structured data efficiently.

Today, we’ll explore how to implement web scraping solutions using native N&N code without relying on third-party platforms. This approach provides a cost-effective way to handle a common client request: transferring existing website data into new systems without manual re-entry.

Understanding Web Scraping Complexity Levels

When approaching web scraping, it’s important to understand that websites vary in complexity:

• Static Single-Page Sites: These are the simplest to scrape, requiring just a few commands to extract all needed information.
• Multi-Page Sites: These require downloading links from the main page, filtering which ones to process, and then making requests to each page before consolidating the information.
• Large Sites (100+ pages): For very large sites with thousands of pages, specialized approaches or finding XML files may be more efficient than direct scraping.

For most client needs, the first two approaches will suffice, as approximately 90% of business websites have fewer than 100 pages.

Implementing a Single-Page Web Scraper

The simple page web scraper works by:

1. Making an HTTP request to the target URL
2. Converting the HTML response to a more readable format (Markdown)
3. Storing the results for quick retrieval in future requests

This approach is ideal for product pages, company information pages, or any static content that lives on a single URL.

A key optimization is implementing a caching mechanism that stores results for a period (e.g., 30 minutes), preventing unnecessary repeated requests to the same page.

Building a Multi-Page Web Scraper

For more complex sites, the multi-page approach follows these steps:

1. Request the main page of the website
2. Extract all links from this page
3. Filter links based on relevance (e.g., only product or collection pages)
4. Loop through each relevant link, making individual requests
5. Transform and store the content from each page
6. Consolidate all information into a comprehensive response

This implementation includes important features like:

• Path filtering to include only relevant sections (e.g., /products/, /collections/)
• Path exclusion for irrelevant areas (e.g., /terms/, /about/)
• Request rate limiting to prevent overloading the target server
• Page count limits to prevent excessive processing

The multi-page scraper is particularly useful for product catalogs, blogs, or any site with structured, paginated content.

Technical Implementation Notes

Both scrapers utilize:

• HTTP requests with retry logic for reliability
• HTML-to-Markdown conversion for better readability
• Memory caching to improve performance for repeated queries
• Proper error handling for network failures

When implementing these solutions, ensure you’re using N&N version 1.95.3 or newer, as earlier versions may not support all the required functions.

Performance Considerations

The performance of web scrapers depends on several factors:

• Target website response time
• Number of pages to process
• Rate limiting configuration
• Network conditions

For the multi-page implementation, a deliberate delay (e.g., 1 second) between requests helps prevent overwhelming the target server while maintaining reasonable scraping speeds.

Conclusion

Web scraping with native N&N code provides a powerful, cost-effective solution for data extraction without third-party dependencies. By understanding the complexity of your target website and implementing the appropriate scraper type, you can efficiently extract and utilize web data for various business applications.

Whether you need to populate a product database, monitor competitor information, or integrate legacy web content into new systems, these web scraping techniques offer a practical approach to solve real business problems.