Building a production-ready web scraper is a complex task, as highlighted in a case study from DEV Community. The author faced the challenge of scraping product listings from an e-commerce site, handling over 10,000 products daily. To achieve this, they chose domharvest-playwright, a tool that balances simplicity with the ability to render JavaScript-heavy pages.

The scraper was designed to run at 2 AM UTC, manage pagination, detect unchanged products, and alert on failures, with results stored in a PostgreSQL database. The architecture involved a cron job triggering a scraper worker, which processed pages and extracted product data. The implementation included handling pagination, extracting structured data, detecting changes to minimize database writes, and robust error handling with alerts.

Challenges such as memory leaks, flaky selectors, and rate limiting were addressed with solutions like batch processing, fallback selectors, and randomized delays. After three months in production, the scraper achieved 99.2% uptime, scraping around 300,000 products with minimal manual intervention. This case study underscores the importance of batch processing, change detection, and monitoring in building reliable scrapers.