What is distributed web crawling?

Distributed web crawling spreads crawling tasks across multiple machines working in parallel rather than relying on a single server. This approach enables crawling millions of pages quickly by partitioning URLs among worker nodes, each with its own resources and network connection. While single-node crawlers handle 60-500 pages per minute, distributed systems achieve 50,000+ requests per second by coordinating work through message queues and shared URL frontiers.

Distributed web crawling is a technique where multiple computers coordinate to crawl websites simultaneously. Instead of one machine handling all requests, the system partitions URLs across worker nodes that operate independently. Each node fetches pages, extracts links, and processes content while communicating through shared infrastructure.

The architecture typically includes a central coordinator managing the URL frontier, worker nodes performing actual crawling, and distributed storage for results. Workers pull URLs from shared queues, crawl assigned pages, and push discovered links back to the frontier. This parallel processing dramatically increases throughput while maintaining coordination to prevent duplicate crawling.

The scheduler coordinates work distribution through message queues like Kafka, RabbitMQ, or Redis. It manages the URL frontier, ensuring each URL gets crawled once, and partitions URLs by domain for politeness.

Worker nodes are stateless processes that pull URLs from queues, fetch content, and extract links. Multiple workers run identical code on different URLs simultaneously. When workers crash, others continue without disruption.

Storage layers separate content from metadata. Object stores handle raw HTML while databases track crawl status and extracted data, allowing concurrent writes without blocking.

Dynamic assignment uses a central server to distribute URLs to workers in real-time, balancing load based on worker availability. This allows adding or removing workers during crawls but creates potential coordinator bottlenecks.

Static assignment uses hash functions to partition URLs by hostname. Each worker handles specific domains through consistent hashing, eliminating coordination overhead but requiring careful load balancing planning.

Geographic distribution places crawler nodes near target servers, reducing latency and respecting data location preferences, though domain names don’t always reflect actual server locations.

Distributed crawlers scale linearly by adding nodes. Need to crawl twice as fast? Add twice as many workers. Single-node crawlers hit hard limits on resources that no optimization can overcome.

Fault tolerance emerges from redundancy. When one worker fails, others continue without interruption. Distributed systems self-heal by reassigning work from failed nodes to healthy ones. Speed improvements are dramatic, with 100 workers achieving 50,000+ requests per second versus single-node limits of 60-120 pages per minute.

Use distributed crawling when single nodes can’t meet throughput requirements. Crawling millions of pages daily, monitoring thousands of websites, or tracking 50,000 product prices requires parallel processing across multiple machines.

Avoid distribution for crawls under 10,000 pages. Coordination complexity, shared state management, and distributed failure handling outweigh benefits for modest workloads. Single-node crawlers with optimization handle most sites efficiently.

Distributed web crawling partitions work across multiple machines, enabling massive scale and fault tolerance impossible with single nodes. The architecture requires shared URL frontiers, stateless workers, and distributed storage for coordination. URL partitioning uses dynamic assignment with central coordination or static assignment via hash functions. Benefits include linear scaling, automatic failover, and 30x faster throughput. Use distribution for million-page crawls requiring high throughput, but stick with single-node architectures for smaller projects where coordination overhead exceeds scaling benefits.