A practical guide to e-commerce data collection
Handling the anti-crawl mechanism of e-commerce websites with Open Operator requires special configurations and skill sets:
Core implementation program
- Dynamic behavioral simulation:
Create ecommerce.ts in the examples directory and configure it using Agent:
- Stochastic rolling amplitude
- Non-uniform mouse trajectory
- Fluctuations in page dwell time
These parameters can significantly reduce the probability of being recognized. - Data Extraction Strategy:
Combined with Stagehand's DOM monitoring capabilities:
1. Listen to the MutationObserver event of the product list.
2. Using XPath to locate dynamic elements such as price/inventory
3. Addressing access frequency limitations through proxy IP rotation - Distributed Scaling:
Combining the Browserbase Cloud Browser with Kubernetes to realize:
- Example of Auto Scaling Browser
- Geographically Distributed IP Allocation
- Asynchronous task queue management
Typical code examples show how to bypass Cloudflare validation: perform manual operation simulation training before applying a reinforcement learning model to generate a sequence of operations.
This answer comes from the articleOpen Operator: Performing Automation in Cloud Browsers with AI IntelligenceThe
May not be reproduced without permission:AI productivity tools " How to apply Open Operator's techniques in e-commerce crawler scenarios?
