Advanced Web Scraping: From LEGO to Production

Today's web landscape is teeming with JavaScript-heavy content, complex layouts, and sometimes opaque data structures. But what if you could reliably scrape rich product information—images, specs, descriptions—from modern e-commerce sites without hitting constant roadblocks? This session tackles advanced scraping with Python, Scrapy, and Playwright, exemplified by data extraction from LEGO product pages. We'll explore a "grey hat" perspective—applying a slightly "hacky" mindset—while stressing practical ethics, performance considerations, and compliance with site policies.

Outline

1. Introduction: The Hacky Spirit vs. Ethical Constraints

• Why scrape LEGO?
• Setting boundaries: terms of service, rate limiting, and disclaimers
• When "scraping for fun" crosses into potential legal pitfalls

2. Scraping Tech Stack Overview

• Scrapy for structured crawling and item pipelines
• Playwright for rendering JavaScript and handling dynamic elements
• Comparison to traditional HTML-only approaches
• Project structure, environment setup, and practical tips

3. Spiders in Action

• Product Spider: Extracting core product data (ID, name, specifications, multiple images)
• Gallery Spider: Navigating hidden galleries, handling tricky JS-based carousels, and filtering unwanted images
• Ensuring consistent output (JSON or database ingestion)

4. Model Context Protocol (MCP) Integration

• Definition: Leveraging specialized helper servers for orchestrating data fetching, refining selectors, and automating debugging
• Chaining Large Language Models: Code suggestions, auto-generation of selectors, and reactive error handling
• Example workflow: "Broken selector? Ask the MCP server for an LLM-aided fix"

5. Performance & Scale

• Polite but robust concurrency: balancing speed and TOS compliance
• Handling large link lists, incremental updates, and site changes
• Monitoring and logging for reliability, debugging, and optimization

6. Ethics & Privacy

• Respecting site ownership, disclaimers, and usage limits
• Storing scraped data securely and avoiding personal information
• A discussion of "grey hat" territory: testing site vulnerabilities without exploiting them

7. Use Cases & Extensions

• Research software engineering: building reproducible data sets
• Robotics and embedded: offline or partial data ingestion for classification or motion planning
• Future directions: advanced concurrency, containerization, and HPC

8. Demo & Q&A

• Live snippet showing an MCP-powered spider reacting to a changed DOM structure
• Q&A session on bridging the gap between hackery and best practices

Key Takeaways

• Techniques for scraping dynamic, JS-heavy sites using Python, Scrapy, and Playwright
• Practical "hacky" methods balanced by responsible, 'ethical approaches'
• Introduction to Model Context Protocol servers for automated code refinement
• Scalable patterns for data handling, from small tests to large-scale deployments

Whether you're a data engineer, hobbyist, or researcher, this talk provides a robust (and slightly subversive) recipe for capturing essential data from the wild world of modern websites—without crossing into unethical or unlawful territory.