Building a price tracker is straightforward, but keeping it running long-term is challenging. How do you handle selector drift and anti-bot measures in your Crawlee projects?
Original source
What the source supports
The source provides a step-by-step tutorial on building a price tracker using Crawlee for Python and Apify. It covers project setup, customizing selectors for product data, implementing email alerts via an Apify actor, and deploying the solution to the cloud with scheduled runs.
What AtlasRepo adds
AtlasRepo structures the tutorial into a clear, actionable guide for developers, emphasizing the separation of concerns between data extraction, logic, and deployment. It highlights the importance of data type conversion and error handling in production environments.
Key takeaways
- Use the Apify CLI to scaffold a Python project with pre-built Crawlee templates.
- Extract structured data by targeting specific CSS selectors and HTML attributes.
- Convert string prices to floats to enable accurate numerical comparisons.
- Trigger external actors, such as email services, based on conditional logic.
- Deploy and schedule crawlers on the Apify cloud for continuous monitoring.
Questions for the community
- What strategies do you use to detect when a website’s HTML structure has changed?
- How do you manage API costs when scheduling frequent runs on the Apify platform?