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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 11·5 min read
Smart Compaction: Predicting Compaction Utility from Lakehouse Table Metadata
TL;DR
A simulation framework was developed to predict the utility of data compaction in lakehouse table formats using metadata features. The model achieved high accuracy in predicting file-reduction ratios, revealing that a simple threshold can often suffice for compaction decisions.
✦ Why It Matters
Evaluate your lakehouse table configurations today to optimize query performance through effective compaction strategies.
Key Takeaways
- Implement compaction when max_files_per_partition exceeds four.
- Use the simulation framework to analyze your table metadata.
- Leverage XGBoost for predicting compaction utility.
- Monitor query performance impacts post-compaction.
How It Works
The framework generates a diverse set of Apache Iceberg tables and extracts metadata features from manifest files. It then employs the XGBoost model to predict the file-reduction ratio, achieving high accuracy.
The study identifies a simple threshold for compaction decisions, streamlining the process.