How do I scrape pages from many domains in bulk?

To scrape pages from many domains in bulk, the main challenge is scale plus variability: different sites have different HTML, rate limits, robots rules, and anti-bot measures. The safest and most maintainable approach is usually to build a domain-aware crawler pipeline rather than one generic scraper.

1) Decide your goal

First clarify whether you need:

• A few known fields from each site
• Full page content
• Links/media extraction
• Recurring updates or one-time collection

This affects architecture a lot.

2) Use a crawler framework, not ad-hoc scripts

For bulk multi-domain scraping, good options are:

• Scrapy (Python): best for high-throughput crawling and scheduling
• Playwright or Selenium: for JS-heavy sites
• Requests + BeautifulSoup/lxml: for simpler sites, but less scalable alone

A common pattern:

• Use Scrapy for discovery and fetching
• Use Playwright only for pages that need rendering

3) Build a domain-aware queue

Create a job queue where each URL includes metadata like:

• domain
• crawl depth
• priority
• crawl delay / politeness settings
• parse template/type

Example:

• example.com pages may use parser A
• news-site.org pages may use parser B

This avoids trying to force one parsing strategy onto everything.

4) Respect robots.txt and site policies

Before crawling:

• Check robots.txt
• Read terms of service if relevant
• Keep request rates low
• Identify your crawler with a clear User-Agent
• Provide contact info if appropriate

5) Throttle per domain

To avoid getting blocked and to be polite:

• Limit concurrency per domain
• Add random jitter between requests
• Cache responses where possible
• Retry with backoff on 429/503
• Pause domains that start failing

Typical settings:

• 1–2 concurrent requests per domain
• 0.5–3 seconds delay, depending on site
• Lower speeds for smaller sites

6) Separate discovery from extraction

A robust pipeline often looks like this:

Discovery phase

• Start from seed URLs
• Follow allowed links
• Collect page URLs into a queue
• Deduplicate URLs

Fetch phase

• Download HTML / rendered content
• Store raw response

Extraction phase

• Parse fields from HTML
• Normalize data
• Validate and dedupe records

Keeping raw HTML is useful so you can re-parse later without re-crawling.

7) Handle different site structures

For many domains, you usually need one of these:

• Per-domain templates: best when sites are known
• Rule-based extraction: CSS selectors/XPath per site
• Generic extraction: title, meta description, text body, canonical URL
• ML/LLM-assisted extraction: useful, but should be validated carefully

If the sites are unrelated, “universal scraping” is usually brittle.

8) Store and dedupe carefully

Use a database or object storage for:

• URLs
• fetch status
• timestamps
• hashes of page content
• extracted records

Deduping strategies:

• canonicalize URLs
• remove tracking params
• hash normalized content
• track already-seen URLs

9) Implement failure handling

Expect:

• timeouts
• redirects
• captchas
• blocked IPs
• missing pages
• changed layouts

Add:

• retries with exponential backoff
• circuit breakers per domain
• logging for parse failures
• alerting for spikes in errors

10) Consider proxies only where appropriate

If your use case is legitimate and permitted, you may need:

• outbound proxy pools
• rotating IPs
• region-specific egress

But don’t rely on proxy rotation to bypass site restrictions. Use it for reliability, geo-routing, or load distribution where allowed.

11) Recommended architecture

A practical bulk scraper stack:

• Scheduler: queues URLs by domain
• Fetcher workers: Scrapy/async HTTP client
• Render service: Playwright for JS pages
• Parser workers: site-specific extraction
• Storage: PostgreSQL for metadata, S3/Blob storage for raw HTML
• Monitoring: logs + metrics + alerts

12) Simple implementation pattern in Python

If you want a straightforward start:

• Use Scrapy for crawling
• Maintain a table of domains and crawl rules
• Use item pipelines to store results
• Add a custom middleware for per-domain rate limiting
• Use Playwright integration only for pages that fail static scraping

13) If you need to scrape many unrelated domains

Then you should strongly consider:

• a generic crawl that collects page text, title, links, metadata
• and a domain-specific extractor only for the domains where you need structured data

That’s usually the only scalable way.

If you want, I can give you:

1. a Scrapy project template for multi-domain crawling, or
2. a Python architecture example with queue + per-domain throttling.