When we first started building our web scraping infrastructure, we were handling a few hundred pages per day. Today, we process over 10 million pages daily. The journey between those two points was filled with architectural missteps, hard-won lessons, and fundamental rethinks of how we approach data collection at scale.
The first major lesson was about distributed architecture. Early on, we ran everything on a single server with a queue-based system. It worked fine until it didn't. When traffic spiked or a target site changed its structure, the entire pipeline would back up. Moving to a distributed architecture with independent workers, each responsible for a subset of domains, was a game-changer.
Rate limiting and polite crawling turned out to be not just ethical requirements but engineering necessities. Sites that detected aggressive crawling would block our IPs, causing cascading failures across our pipeline. We implemented adaptive rate limiting that adjusts request frequency based on server response times and HTTP status codes.
Data quality became our biggest challenge at scale. When you're processing millions of pages, even a 0.1% error rate means thousands of corrupted records. We built a multi-stage validation pipeline: schema validation at ingestion, statistical anomaly detection during processing, and human-in-the-loop review for flagged records.
Storage and indexing required a complete rethink. We moved from a monolithic PostgreSQL database to a hybrid approach: PostgreSQL for structured metadata, S3 for raw HTML snapshots, and Elasticsearch for full-text search. This separation of concerns reduced our storage costs by 60% while improving query performance.
Perhaps the most important lesson was investing in monitoring and observability early. We built dashboards tracking success rates per domain, data freshness, and pipeline throughput. When something breaks at scale, you need to know about it in minutes, not hours.
