SOLAR: Scalable Distributed Spatial Joins through Learning-based Optimization
Yongyi Liu, Ahmed Mahmood, Amr Magdy, Minyao Zhu·April 02, 2025
Summary
SOLAR optimizes scalable distributed spatial joins, enhancing runtime by 3.6X and partitioning time by 2.71X. It learns balanced partitioning for spatial data, excelling in managing large-scale datasets. Demonstrated industrial suitability, with continuous data integration showing scalability and improved performance with larger training data. SOLAR outperforms competitors, achieving up to 3.52X speedup for familiar joins and 2.69X for unseen ones. Using a Siamese Neural Network, it optimizes joins, especially at smaller distances, with strong generalization and partitioner reuse benefits. Future work aims for broader spatial join support and self-improvement in database systems.
Introduction
Background
Overview of spatial data and its challenges in distributed systems
Importance of efficient spatial join operations in large-scale datasets
Objective
Enhancing runtime and partitioning time for spatial joins
Achieving scalability and improved performance with larger datasets
Method
Data Collection
Techniques for gathering spatial data in distributed environments
Data Preprocessing
Methods for preparing spatial data for efficient join operations
Learning Balanced Partitioning
Utilization of machine learning for optimal spatial data partitioning
Siamese Neural Network
Application of a Siamese Neural Network for spatial join optimization
Focus on improving performance at smaller distances
Generalization and Partitioner Reuse
Benefits of strong generalization and efficient partitioner reuse
Results
Runtime Improvement
Achieved speedup of 3.6X for familiar joins
2.71X reduction in partitioning time
Performance with Larger Datasets
Scalability demonstrated through continuous data integration
Outperformance of Competitors
Up to 3.52X speedup for familiar joins
2.69X speedup for unseen joins
Industrial Suitability
Real-world Applications
Case studies showcasing industrial use of SOLAR
Scalability and Performance
Evidence of SOLAR's ability to handle large-scale datasets effectively
Future Work
Broader Spatial Join Support
Plans for expanding SOLAR's capabilities to support more types of spatial joins
Self-Improvement in Database Systems
Potential for SOLAR to adapt and improve over time within database systems
Basic info
papers
databases
Advanced features
Insights
What are the future directions for SOLAR in terms of broader spatial join support and self-improvement?
In what ways does SOLAR demonstrate industrial suitability and scalability with continuous data integration?
How does SOLAR utilize a Siamese Neural Network to optimize distributed spatial joins?
What are the key implementation strategies of SOLAR that contribute to its enhanced runtime and partitioning time?