I need a recommendation for crawling thousands of URLs across many domains and detecting page changes over time

For thousands of URLs across many domains with change detection over time, I’d recommend a setup built around a scheduled crawler + content fingerprinting + diff storage.

Best approach

1) Crawl with a scalable headless/HTTP fetcher

Use:

• Scrapy if most pages are static HTML and you want throughput
• Playwright or Puppeteer if many pages are JavaScript-rendered
• A hybrid approach if some sites are static and some dynamic

2) Normalize page content before comparing

To detect meaningful changes, strip noise such as:

• timestamps
• ads/trackers
• nav/footer content
• dynamic IDs / session tokens
• whitespace / formatting changes

Then generate:

• hashes for quick change checks
• structured diffs for visible changes
• optionally DOM-based fingerprints for more robust comparison

3) Store snapshots and change history

Keep:

• raw HTML or rendered text
• extracted cleaned content
• hashes/fingerprints
• crawl metadata: URL, domain, fetch time, status code, response headers

4) Schedule recrawls intelligently

Don’t crawl everything equally often. Use:

• per-domain rate limits
• crawl frequency based on change likelihood
• retries/backoff for failures
• conditional requests (ETag, If-Modified-Since) where possible

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Recommended stack options

Option A: Open-source, flexible

• Scrapy for crawling
• Playwright for JS-heavy pages
• PostgreSQL or MongoDB for metadata/snapshots
• S3/Blob storage for raw HTML
• Redis for queueing and deduplication
• A diff service using:
  • SHA-256 hashes for quick detection
  • difflib, diff-match-patch, or a DOM diff library for comparison

Best if you want control and lower cost.

Option B: Managed crawling platform

If you want less infrastructure work:

• Apify
• Zyte
• Bright Data
• Diffbot for extraction-focused workflows

Best if you need scale quickly and don’t want to manage anti-bot and rendering complexity.

Option C: Distributed architecture

If you expect growth:

• Kafka / RabbitMQ / SQS for task queues
• Kubernetes workers for horizontal scale
• separate services for:
  • URL scheduling
  • fetching/rendering
  • content extraction
  • diffing/change detection
  • alerting/reporting

Best for large, long-running systems.

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Practical recommendation

If you’re building this yourself, I’d start with:

• Scrapy for scheduling and fetching
• Playwright only for pages that need JS
• PostgreSQL + S3
• Redis for job queueing
• content hashing + normalized text diff
• ETag / Last-Modified support
• per-domain crawl throttling

This gives a good balance of cost, scale, and maintainability.

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Key design tips

• Store one snapshot per crawl so you can compare over time.
• Compare both full-page fingerprints and important sections.
• Separate “page changed” from “meaningful change”.
• Build alerts only when change exceeds a threshold.
• Respect robots.txt and legal/ToS constraints.

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If you want, I can also give you:

1. a reference architecture diagram,
2. a technology comparison table, or
3. a starter implementation plan in Python.