How do I crawl thousands of websites automatically?

To crawl thousands of websites automatically, you want a crawler pipeline, not just a single script. The basic approach is:

1) Define scope and rules first

• Only crawl sites you’re allowed to crawl.
• Check robots.txt, site terms, and rate limits.
• Decide what you want to extract: pages, product data, links, metadata, PDFs, etc.

2) Use a crawler framework

For scale, use one of these:

• Scrapy (Python): great for fast, large-scale crawling
• Playwright / Selenium: for JavaScript-heavy sites
• Apache Nutch / Heritrix: more traditional large web crawling
• Puppeteer: if you prefer Node.js

A common setup:

• Scrapy for most pages
• Playwright only when JS rendering is required

3) Build a URL queue

You need a system to manage millions of URLs safely:

• Start with a seed list of domains/URLs
• Normalize URLs to avoid duplicates
• Maintain:
  • frontier queue of URLs to visit
  • visited set or database
  • allowed_domains
  • crawl depth / page limits

For scale, use:

• Redis
• Kafka
• RabbitMQ
• PostgreSQL or MongoDB for persistence

4) Respect rate limits

To avoid blocking:

• Limit requests per domain
• Add random delays
• Use concurrency carefully
• Retry failed requests with backoff

Example concepts:

• 1–2 requests/sec per domain
• 5–20 concurrent requests overall, depending on infrastructure

5) Distribute the crawl

If you’re crawling thousands of websites:

• Run multiple workers
• Each worker pulls URLs from the central queue
• Store results centrally
• Use containers and orchestration if needed:
  • Docker
  • Kubernetes
  • Celery workers
  • Redis queue

6) Handle failures and duplicates

You’ll need:

• Deduplication by canonical URL or content hash
• Retry policies
• Logging for errors and blocked requests
• Checkpointing so crawls can resume

7) Extract and store data

Typical output:

• URL
• page title
• status code
• crawl timestamp
• HTML/text
• extracted fields

Store it in:

• PostgreSQL for structured data
• Elasticsearch/OpenSearch for search
• S3/object storage for raw HTML

8) Monitor the crawl

Track:

• requests/sec
• error rate
• blocked domains
• depth coverage
• CPU/memory usage

9) Start small

Prototype on 10–20 sites first, then scale up.

---

Simple example stack

If you want a practical stack:

• Scrapy + Redis
• PostgreSQL for metadata
• S3 for raw pages
• Playwright for dynamic sites
• Docker for deployment

---

Example architecture

1. Seed list of 5,000 domains
2. Scheduler pushes root URLs into Redis
3. Worker crawlers fetch pages
4. Extract links and add new URLs back to queue
5. Store page content and metadata
6. Separate processing pipeline cleans and indexes data

---

Important cautions

• Don’t crawl aggressively; many sites will block you.
• Respect robots.txt and legal restrictions.
• Avoid scraping personal data unless you have a lawful basis.

If you want, I can give you:

1. a Scrapy project template,
2. a distributed crawl architecture, or
3. a Python example that crawls multiple domains safely.