Breaking Performance Bottlenecks with Distributed Computing Architecture
Shortcut's optimization strategy for massive data processing:
- Intelligent chunk loading: Automatic segmentation in 100MB units to avoid memory overflow
- Columnar Storage Optimization: Adopt compression algorithm for numeric fields to improve calculation speed by 3-5 times.
- Backend Preprocessing: Automatic indexing during upload, response time for subsequent operations <500ms
Operational Recommendations:
- Delete extraneous worksheets before uploading, retain core data columns
- Use "filter by condition" to narrow down the scope of the process first (e.g., "Calculate data for 2024 only")
- Decomposition of complex tasks into serial execution of multiple instructions ("sorting and summarizing" and then "ordering")
Real-world data: In the Excel World Championship, Shortcut processed 150,000 rows of sales records in just 8 minutes and 23 seconds, compared to an average of 2 hours for the same task manually.
This answer comes from the articleShortcut: the AI smart assistant for automating Excel tasksThe
May not be reproduced without permission:AI productivity tools " How to achieve fast processing of very large Excel datasets (100,000+ rows)?
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