Need to crawl millions of pages into a database

To crawl millions of pages into a database, you’ll want a distributed, fault-tolerant crawling pipeline rather than a single script. The main pieces are:

1) Core architecture

• URL Frontier / Queue
  • Stores URLs to visit
  • Deduplicates URLs
  • Prioritizes by domain, freshness, or depth
• Fetcher Workers
  • Download pages concurrently
  • Respect robots.txt, rate limits, and retries
• Parser/Extractor
  • Extracts links, metadata, content, canonical URLs
• Storage
  • Raw HTML in object storage or blob store
  • Parsed records in a database
• Scheduler
  • Decides what to crawl next and when to revisit
• Monitoring
  • Tracks throughput, errors, bans, queue depth, latency

2) Recommended technology choices

If you want to build fast:

• Queue: Kafka, RabbitMQ, Redis Streams, or SQS
• Workers: Python asyncio, Go, or Node.js
• Raw page storage: S3 / GCS / MinIO
• Database:
  • Postgres for metadata at moderate scale
  • Cassandra / DynamoDB / Bigtable for very high write volume
  • Elasticsearch/OpenSearch for search indexing
• Deduplication: Bloom filter + canonical URL normalization

If you expect very large scale:

• Use:
  • Kafka for frontier events
  • Many stateless crawler workers
  • S3 for HTML
  • Postgres/Cassandra for extracted structured data
  • Redis or RocksDB for per-domain crawl state

3) Important crawler behaviors

• Normalize URLs
  • Remove tracking params
  • Normalize scheme/host/path
• Avoid duplicates
  • URL hash
  • Content hash for near-duplicate detection
• Respect robots.txt
• Use polite concurrency
  • Per-domain concurrency limits
  • Backoff on 429/503
• Retry policy
  • Retry transient failures
  • Dead-letter queue for repeated failures
• Canonicalization
  • Prefer canonical tags when available

4) Database schema idea

You generally want separate tables/collections for:

• pages
  • url, url_hash, status_code, fetched_at, content_type, html_location, content_hash
• links
  • source_url, target_url, anchor_text
• crawl_state
  • domain, last_fetched_at, next_fetch_at, robots_allowed
• errors
  • url, error_type, attempts, last_error

5) Crawling strategy

• Seed with an initial list of URLs
• Expand by following links
• Prioritize:
  • New domains
  • Important pages
  • Pages with fresh content
• Schedule recrawls based on change frequency

6) Scaling tips

• Make workers stateless
• Partition by domain hash
• Store large HTML outside the DB
• Use batch writes
• Compress content
• Keep DB writes idempotent
• Use asynchronous fetching

7) Minimal system design

A practical setup:

1. Seed URLs into Kafka/Redis
2. Workers fetch pages concurrently
3. Parser extracts links and metadata
4. Write HTML to S3
5. Write metadata and links to DB
6. Push discovered URLs back to queue
7. Enforce dedupe and per-domain rate limits

8) Suggested stack for a solid implementation

If you want a good balance of simplicity and scale:

• Python + asyncio
• Kafka or Redis Streams
• S3-compatible storage
• PostgreSQL for metadata
• Redis for dedupe and host politeness
• Docker + Kubernetes for scaling workers

9) Example crawling flow

• URL in queue
• Check if allowed by robots.txt
• Check dedupe store
• Fetch page
• Save raw HTML
• Parse and extract links
• Insert page record
• Add new URLs to frontier
• Update domain crawl schedule

If you want, I can also provide:

1. a system architecture diagram,
2. a Postgres schema, or
3. a Python crawler skeleton that can scale to millions of pages.