Distributed Scraping

Distributed scraping is a web data extraction technique that leverages multiple machines, servers, or cloud instances to parallelize and scale the process of collecting data from websites. Instead of relying on a single system, distributed scraping spreads requests across multiple nodes to improve efficiency, reduce detection risks, and handle large-scale data retrieval.

Also known as: Scalable web scraping, parallelized scraping.

Comparisons

• Distributed Scraping vs. Single-Node Scraping: Single-node scraping runs on one machine, limiting speed and scalability, while distributed scraping distributes the workload for better performance.

• Distributed Scraping vs. Load Balancing: While both techniques manage traffic distribution, distributed scraping specifically focuses on spreading web requests across multiple IPs or locations for data extraction.

Pros

• Increases efficiency by enabling parallel data collection.

• Reduces the risk of IP bans by distributing requests across multiple sources.

• Handles large-scale scraping tasks that exceed the capabilities of a single machine.

Cons

• More complex to set up, requiring orchestration of multiple systems.

• Can introduce higher infrastructure costs compared to single-node scraping.

• Requires managing consistency and deduplication of scraped data.

Example

A company extracting product prices from multiple e-commerce sites deploys a distributed scraping system using cloud-based proxies, containerized scrapers, and task queues. This setup ensures high-speed data retrieval while avoiding detection by distributing requests across different IPs and geographic locations.