crawl websites at scale

Crawling websites at scale is doable, but the right approach depends on your goal: search indexing, price monitoring, lead gen, ML data collection, or site auditing.

Core architecture

A scalable crawler usually has these components:

1. URL frontier

   • Stores URLs to visit
   • Deduplicates
   • Prioritizes by domain, freshness, depth, or importance

2. Fetcher workers

   • Distributed workers that download pages
   • Respect rate limits and robots.txt if required
   • Retry transient failures

3. Parser/extractor

   • Extracts links, metadata, structured data, text, assets
   • Normalizes URLs and canonicalizes content

4. Storage

   • Raw HTML in object storage
   • Parsed data in a database/search index
   • Metadata and crawl state in a fast key-value store

5. Scheduler

   • Decides what to crawl next
   • Enforces per-domain politeness
   • Handles recrawl intervals and backoff

6. Monitoring

   • Throughput, error rates, bans, latency, queue size
   • Domain-level health and dead-letter queues

Key scaling principles

• Partition by domain or host to enforce politeness and simplify rate limiting
• Use async I/O for high connection concurrency
• Keep fetchers stateless; store crawl state centrally
• Deduplicate aggressively
  • URL normalization
  • Content hashing
  • Canonical link handling
• Back off on errors
  • 429/403/5xx handling
  • Adaptive per-host throttling
• Prioritize valuable URLs first
• Support retries and idempotency
• Use a queue-based pipeline so each stage can scale independently

Common tech stack

• Queue/frontier: Kafka, RabbitMQ, Redis Streams, SQS, or custom sharded frontier
• Workers: Go, Python asyncio, Rust, Java
• Storage:
  • S3/GCS for raw pages
  • Postgres/MySQL for metadata at smaller scale
  • Cassandra/Bigtable/DynamoDB for large-scale state
  • Elasticsearch/OpenSearch for indexed content
• Parsing: BeautifulSoup/lxml, trafilatura, readability, Playwright for JS-heavy sites
• Orchestration: Kubernetes, ECS, Nomad

Important considerations

• Legal/ethical

  • Check robots.txt where appropriate
  • Respect ToS and copyright restrictions
  • Avoid abusive request rates
  • Identify your crawler with a user agent and contact info if possible

• Dynamic sites

  • Use headless browsers selectively; they’re expensive
  • Prefer HTTP fetch + HTML parsing when possible

• Politeness

  • Limit concurrency per domain
  • Randomize delays
  • Avoid hammering the same origin

• Data quality

  • Normalize encodings and languages
  • Detect duplicate/spam pages
  • Track fetch time and HTTP status for freshness

Simple scaling pattern

A good baseline design:

• One seed loader adds starting URLs
• A distributed frontier stores (normalized_url, priority, next_fetch_time)
• Many fetcher workers pull eligible URLs
• Each fetched page emits:
  • raw HTML to object storage
  • extracted links back to frontier
  • metadata to the database
• A recrawl scheduler re-enqueues pages based on change frequency

If you want to build this

I can help you with any of these next:

1. a reference architecture
2. a Python/Go crawler skeleton
3. a distributed design with Redis/Kafka/SQS
4. a polite crawler with rate limiting and robots.txt
5. a site-specific scraping setup for JS-heavy pages

If you want, I can draft a concrete architecture for 10k, 1M, or 100M pages/day.