High-volume web crawler

If you mean a crawler that can fetch a lot of pages reliably and politely, the core design usually looks like this:

Core components

• URL frontier / scheduler: queue of URLs to fetch
• Fetcher workers: concurrent HTTP clients
• Deduplication: avoid revisiting the same URL/content
• Parser/extractor: pull links and data from pages
• Politeness layer: per-host rate limits, robots.txt checks, backoff
• Storage: raw HTML, metadata, extracted records
• Monitoring: error rates, throughput, block detection

Common architecture

• Use a distributed queue like Kafka, SQS, Redis, or RabbitMQ
• Split work by host/domain so you can enforce crawl delays
• Run many worker nodes with async I/O
• Keep a seen URL set in a fast store like Redis, RocksDB, or a Bloom filter + exact store
• Store fetched pages in object storage, and extracted data in a database/search index

Important limits

• Respect robots.txt
• Rate-limit per domain
• Identify your crawler with a clear User-Agent
• Handle retries, timeouts, and 429/503 responses with exponential backoff

Tech stack examples

• Python: Scrapy, asyncio, aiohttp
• Go: great for high concurrency
• Java/Scala: mature distributed systems
• Crawling frameworks: Scrapy Cluster, Heritrix, Apache Nutch

If you want maximum scale

A practical pattern is:

1. Seed URL list
2. Normalize and dedupe URLs
3. Push into queue by domain
4. Workers fetch pages concurrently
5. Parse links and enqueue new URLs
6. Persist content + metadata
7. Continuously monitor crawl health and politeness

If you want, I can help with any of these next:

1. a system design for a high-volume crawler,
2. a Scrapy-based implementation, or
3. a distributed architecture with queue, workers, and dedupe.