MiroFlow's highly concurrent architecture is designed to address data collection stability issues:
- Asynchronous Task Queue: Adopt non-blocking task distribution mechanism, support 500+ concurrent requests
- Intelligent Rate Control: Automatically detect API service limitations and dynamically adjust request frequency
- burst transmission mechanism: network interruptions can continue to collect from the most recent success point, data integrity checks using the CRC32 algorithm
- Multi-service redundancy: Integrate Serper/Jina and other multi-source crawler services, and automatically switch when a single service fails.
become man and wifeHTTPS_PROXYEnvironment variable configuration can further enhance the success of transnational data collection.
This answer comes from the articleMiroFlow: a framework for building, managing and scaling AI intelligencesThe
May not be reproduced without permission:AI productivity tools " How to achieve stability assurance during large-scale data collection?
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