Recently, I encountered IP risk control problems when optimizing distributed crawlers (especially training multimodal models requires cross-regional data), and tested several solutions:
Self-built proxy pool: high maintenance cost, difficult to obtain residential IP
Open source proxy tools: Cloudflare verification code rate > 37%
Commercial proxy service: Thordata’s intelligent routing solution was finally adopted, sharing key parameters:
Dynamic IP pool with automatic geo-randomization (195+ countries)
Request header fingerprint obfuscation + HTTPS traffic feature simulation
Adaptive concurrency control (measured API success rate 98.6%)
Actual measurement comparison:
Traditional proxy: average collection speed 12.3 req/s, ban rate 22%
Thordata solution: speed increased to 38.7 req/s, ban rate <0.4%
Note: Too high a request frequency will still trigger anti-crawl (it is recommended to combine request interval randomization + User-Agent polling)
Technical questions:
How do you handle IP resource scheduling when collecting tens of millions of data? Is there a better open source toolchain recommendation?
(Attached is the Thordata technical white paper for reference → [invitation code GW4ZZXWC] Non-commercial sharing, please allow it if not specified by the administrator)