A Modular Graph-Native Query Optimization Framework
Bingqing Lyu, Xiaoli Zhou, Longbin Lai, Yufan Yang, Yunkai Lou,, Wenyuan Yu, Jingren Zhou

TL;DR
GOpt is a modular, graph-native query optimization framework that supports multiple query languages, decouples execution from specific systems, and employs advanced optimization techniques to significantly improve performance.
Contribution
It introduces a flexible, modular architecture with a unified intermediate representation and optimization strategies for complex graph pattern queries.
Findings
Neo4j speedup of 9.2x on average
GraphsScope speedup of 33.4x on average
Significant performance improvements on real-world datasets
Abstract
Complex Graph Patterns (CGPs), which combine pattern matching with relational operations, are widely used in real-world applications. Existing systems rely on monolithic architectures for CGPs, which restrict their ability to integrate multiple query languages and lack certain advanced optimization techniques. Therefore, to address these issues, we introduce GOpt, a modular graph-native query optimization framework with the following features: (1) support for queries in multiple query languages, (2) decoupling execution from specific graph systems, and (3) integration of advanced optimization techniques. Specifically, GOpt offers a high-level interface, GraphIrBuilder, for converting queries from various graph query languages into a unified intermediate representation (GIR), thereby streamlining the optimization process. It also provides a low-level interface, PhysicalSpec, enabling…
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Taxonomy
TopicsGraph Theory and Algorithms · Advanced Database Systems and Queries · Cloud Computing and Resource Management
