A General Cardinality Estimation Framework for Subgraph Matching in Property Graphs

TL;DR

This paper introduces a comprehensive framework for estimating the number of results in subgraph matching queries over property graphs, enabling analysis, comparison, and combination of various estimation techniques.

Contribution

It proposes a three-phase, modular framework that unifies existing approaches and facilitates the development of new techniques for accurate cardinality estimation in property graph databases.

Findings

Existing approaches can be integrated into the framework's phases.

Accurate estimates for small patterns without property constraints.

Need for new techniques to handle property constraints effectively.

Abstract

Many techniques have been developed for the cardinality estimation problem in data management systems. In this document, we introduce a framework for cardinality estimation of query patterns over property graph databases, which makes it possible to analyze, compare and combine different cardinality estimation approaches. This framework consists of three phases: obtaining a set of estimates for some subqueries, extending this set and finally combining the set into a single cardinality estimate for the query. We show that (parts of) many of the existing cardinality estimation approaches can be used as techniques in one of the phases from our framework. The three phases are loosely coupled, this makes it possible to combine (parts of) current cardinality estimation approaches. We create a graph version of the Join Order Benchmark to perform experiments with different combinations of techniques. The results show that query patterns without property constraints can be accurately estimated using synopses for small patterns. Accurate estimation of query patterns with property constraints require new estimation techniques to be developed that capture correlations between the property constraints and the topology in graph databases.