Web Scraping at Scale: From 1K to 10M Pages

The Scale Problem Scraping 100 pages is a script. Scraping 10 million pages is an engineering challenge. As you scale web scraping, every part of your system gets stressed — network I/O, CPU, memory, storage, and proxy costs. I've built scrapers that process millions of pages. Here's what actually matters at scale. The Scaling Tiers Scale Pages Architecture Typical Infra Small 1-10K Single script Laptop Medium 10K-100K Async + queue Single server Large 100K-1M Distributed workers Multiple servers Massive 1M-10M+ Full pipeline Cloud + managed services Tier 1: Getting to 10K Pages The first optimization: go async. A synchronous scraper hitting one page at a time wastes 95% of its time waiting for network responses. Synchronous (Slow) import requests import time def scrape_sync ( urls ): results = [] for url in urls : response = requests . get ( url ) results . append ( parse ( response . text )) return results # 1000 pages at 1s each = ~17 minutes Async (Fast) import asyncio import aioht