Building Scalable Web Scrapers with Flask and Celery

Building a web scraper that monitors multiple sources reliably is harder than it looks. The naive approach — a single script that fetches pages in sequence — breaks the moment you need to track more than a few sources with different update schedules.

The key insight was separating the scheduling logic from the scraping logic using Celery. The beat scheduler dispatches tasks at configurable intervals, and worker processes handle the actual HTTP requests and parsing concurrently. This gives us horizontal scaling for free — add more workers as you add more sources.

Rate limiting was the next challenge. Each source has different tolerance for request frequency. I implemented an adaptive rate limiter that tracks response times and HTTP status codes, backing off when servers respond slowly or return rate-limit errors.

Deduplication turned out to be the most interesting problem. Different sources might post the same chapter with slightly different titles or formatting. I built a fuzzy matching system that compares normalized titles, content fingerprints, and publish timestamps to identify duplicates without false positives.

The final piece was the notification system. Rather than polling for updates, the pipeline pushes notifications through configurable channels — email via SendGrid and webhooks for Discord integration. Each user can set their notification preferences independently.