Advanced Performance Optimization Solutions
The following optimization strategies are recommended for massive data collection tasks:
- System-level optimization::
- Running tools on Linux servers to avoid resource constraints in Windows environments
- set up
--max-connections 10Control the number of concurrent connections (adjusted according to server configuration)
- Task slicing technology::
- Segment large collection tasks into time segments:
node index.js hashtag 标签 --since 20230101 --until 20230115 - utilization
splitcommand to split large user list files
- Segment large collection tasks into time segments:
- Memory Management Tips::
- increase
--no-cacheParameter to disable memory caching (good for long-running tasks) - Regular use
history -cClear History
- increase
- Optimization of results processing::
- utilization
--outputparameter writes the result directly to the database (requires configuration of a connection such as MySQL) - start using
--quietMode reduces console output overhead
- utilization
Surveillance recommendation: cooperatepm2and other process management tools to monitor memory usage and automatically restart the task when it exceeds 80%. For ultra-large-scale collection (100,000+ data), distributed deployment of multiple collection nodes is recommended.
This answer comes from the articleTikTok Scraper: batch download TikTok videos with data collection toolThe
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