A Graph-native Optimization Framework for Complex Graph Queries

2025-03-28

AI Summary

Optimizing complex graph queries across diverse semantic interpretations (e.g., homomorphism vs. edge-distinguishing matching) and heterogeneous backends (e.g., Neo4j, GraphScope) remains challenging due to semantic heterogeneity and backend-specific physical execution constraints. Method: This paper proposes GOpt, a graph-native optimization framework centered on a unified Graph Intermediate Representation (GIR). GOpt introduces the first semantics-aware, rule-based optimization system that formally characterizes how semantic distinctions affect query plan generation. It further defines a pluggable physical operator interface to enable declarative query compilation and backend-specific physical-layer integration. Results: Evaluated on the LDBC benchmark, GOpt significantly improves both optimization efficiency and end-to-end query performance for complex workloads. It ensures optimization consistency across backends and demonstrates cross-engine scalability—thereby validating the effectiveness and generality of the graph-native optimization paradigm.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This technical report extends the SIGMOD 2025 paper"A Modular Graph-Native Query Optimization Framework"by providing a comprehensive exposition of GOpt's advanced technical mechanisms, implementation strategies, and extended evaluations. While the original paper introduced GOpt's unified intermediate representation (GIR) and demonstrated its performance benefits, this report delves into the framework's implementation depth: (1) the full specification of GOpt's optimization rules; (2) a systematic treatment of semantic variations (e.g., homomorphism vs. edge-distinct matching) across query languages and their implications for optimization; (3) the design of GOpt's Physical integration interface, enabling seamless integration with transactional (Neo4j) and distributed (GraphScope) backends via engine-specific operator customization; and (4) a detailed analysis of plan transformations for LDBC benchmark queries.

Problem

• Optimizing complex graph queries efficiently
• Handling semantic variations across query languages
• Integrating with diverse graph database backends

Innovation

• Graph-native optimization rules specification
• Semantic variations systematic treatment
• Physical integration interface design