Prompt
Need bulk website scraping across many domains
Latest observation
If you need to scrape many websites at scale, the safest and most reliable approach is to build a distributed scraping pipeline rather than a single scraper script.
Recommended architecture
1) URL discovery
- Start from a seed list of domains
- Crawl site maps first if available:
/sitemap.xml- sitemap index files
- Fall back to polite crawling from landing pages
2) Fetching layer
Use a queue-based worker system:
- Queue: Redis / SQS / RabbitMQ
- Workers: Python, Node.js, or Go
- Concurrency control: per-domain limits to avoid overloading sites
- Retry logic: exponential backoff, timeouts, circuit breakers
3) Parsing/extraction layer
Depends on what you need:
- Structured data: JSON-LD, microdata, OpenGraph, meta tags
- Text content: main article/body extraction
- Product data: title, price, availability, images
- Company data: contact info, location, etc.
4) Storage
- Raw HTML in object storage: S3/GCS/Azure Blob
- Parsed records in:
- PostgreSQL for moderate scale
- Elasticsearch/OpenSearch for search
- BigQuery/Snowflake for analytics
5) Monitoring
Track:
- success/error rates
- response times
- blocked requests / captchas
- per-domain throughput
- content changes over time
Important compliance notes
When scraping many domains, make sure you:
- respect each site’s robots.txt
- review site terms of service
- avoid bypassing access controls, CAPTCHAs, or rate limits
- identify your crawler with a clear User-Agent and contact info
- use low, respectful request rates
Practical stack options
Simple setup
- Python + Requests/BeautifulSoup
- SQLite/PostgreSQL
- Celery + Redis for task distribution
More robust setup
- Scrapy for crawling
- Playwright only when JavaScript rendering is required
- Redis or SQS for queueing
- Docker + Kubernetes for scaling workers
Large-scale setup
- Scrapy-Cluster or custom distributed crawler
- S3 + Kafka + Spark/BigQuery
- Observability with Prometheus/Grafana
Suggested workflow
- Normalize domain list
- Check robots.txt and sitemap availability
- Enqueue discovered URLs
- Fetch pages with per-domain throttling
- Extract desired fields
- Deduplicate and store results
- Re-crawl on a schedule for updates
If you want, I can help with one of these next:
- a Python bulk scraper template
- a Scrapy-based distributed crawler
- a domain discovery + sitemap crawler
- a database schema for storing scraped data
- a deployment plan for scraping thousands of sites
If you tell me:
- how many domains/pages,
- whether pages are static or JS-heavy,
- and what data you want to extract,
I can recommend a concrete setup and provide starter code.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (2 observations for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.