I'm in the process of creating a real estate search engine and I've hit a snag with scraping listings from various portals. The issue is that each site has its own layout, making it a challenge to write and test CSS selectors effectively. Once I finally get a selector to work, it often breaks within a couple of weeks due to changes on the site. I'm looking for advice on how to keep up with these changes and make the scraping process more efficient.
5 Answers
I've been experimenting with Playwright MCP along with AI agents. This setup lets AI determine the scraping path based on real-time data instead of relying solely on predefined commands. Just a heads-up though, watch the costs associated with some AI models; larger ones can be pricey, but there are cheaper nano models out there that work well!
When dealing with changing HTML structures, I've found that implementing robust error handling helps a lot. It wasn't my strong suit at first, but now I get clear error messages that tell me what's wrong in the code when something doesn't match. I also create lists of possible tag phrases to increase my chances of finding what I need. Plus, I focus on uncovering hidden APIs that many sites have available; they tend to be a lot more stable than scraping HTML.
Maybe consider stopping the practice of scraping content from other sites. It’s important to respect copyright and content ownership.
Have you looked into Oxylabs' new Parsing Instruction Generation API? It generates parsing rules based on prompts or JSON schemas and features self-healing capabilities, which can really cut down on the time you spend maintaining scrapers.
How are you scrapping the data? Understanding your approach might help others give more targeted suggestions.
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