Implementation Strategies for Efficient Data Crawling
Web data crawling often faces problems such as anti-crawl restrictions, structure changes, data cleaning, etc. Airtop's solution has the following advantages:
- Intelligent Element Recognition: Specify the crawling target in natural language (e.g. "extract all elements with .price class name").
- Adaptive Paging: Automatically recognizes and handles paging navigation for full data collection
- Structured Output: Directly generate data in JSON format, support API docking and file exporting
Best Practices:
- Use precise target descriptions (compare "Extract data" with "Extract product name, price and stock status")
- Work with CSS selectors to improve accuracy (e.g. "extract h3 tag text under div.product-list")
- Setting reasonable intervals between operations (bans can be avoided by "waiting 2 seconds before clicking on the next page")
- Automate Data Inbound with API Integration
Actual tests show that this method can improve the efficiency of e-commerce data collection by more than 8 times. For dynamically loaded content, it is recommended to work with commands such as "scroll to the bottom of the page" to ensure that the data is loaded completely.
This answer comes from the articleAirtop: A Browser Automation Tool Using Natural Language ControlsThe
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