Challenge analysis
Traditional approaches face storage and performance bottlenecks when dealing with billion-data sets such as ClueWeb22.
Optimization solutions
- Tiered storage architecture:SSD stores hot data, HDD stores historical data
- Distributed processing:Start multiple threads with the num_workers parameter. 1-2 workers per physical core is recommended.
- Batch processing:Set num_selected_docs_per_iter to control the amount processed per batch (10000 recommended)
- Results compression:Output files are compressed with gzip to save space
Management Skills
- Periodically execute fetch_docs.py to convert IDs to text, freeing storage space
- Quickly validate specific document quality using the access_data.py script
- Output catalog management by date/project
Stable handling of 20 million+ volume document crawling tasks after implementation.
This answer comes from the articleCrawl4LLM: An Efficient Web Crawling Tool for LLM PretrainingThe
May not be reproduced without permission:AI productivity tools " How to achieve efficient crawling and management of large-scale datasets?
