A Graph-Native Query Optimization Framework
CoRR(2024)
摘要
Graph queries that combine pattern matching with relational operations,
referred as PatRelQuery, are widely used in many real-world applications. It
allows users to identify arbitrary patterns in a graph and further perform
in-depth relational analysis on the results. To effectively support
PatRelQuery, two key challenges need to be addressed: (1) how to optimize
PatRelQuery in a unified framework, and (2) how to handle the arbitrary type
constraints in patterns in PatRelQuery. In this paper, we present a
graph-native query optimization framework named GOpt, to tackle these issues.
GOpt is built on top of a unified intermediate representation (IR) that is
capable of capturing both graph and relational operations, thereby streamlining
the optimization of PatRelQuery. To handle the arbitrary type constraints, GOpt
employs an automatic type inference approach to identify implicit type
constraints. Additionally, GOpt introduces a graph-native optimizer, which
encompasses an extensive collection of optimization rules along with cost-based
techniques tailored for arbitrary patterns, to optimize PatRelQuery. Through
comprehensive experiments, we demonstrate that GOpt can achieve significant
query performance improvements, in both crafted benchmarks and real-world
applications.
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