SPACE : Cardinality Estimation for Path Queries Using Cardinality-Aware Sequence-based Learning

Abstract

Cardinality estimation is a central task of cost-based database query optimization. Accurate estimates enable optimizers to identify and avoid expensive plans requiring large intermediate results. While cardinality estimation has been studied extensively in relational databases, research in the setting of graph databases has been more scarce. Furthermore, recent studies have shown that machine-learning-based methods can be utilized for cardinality estimation in both relational and graph databases. In this paper, we focus on the problem of estimating the cardinality of path patterns in graph databases, and we propose the Sequence-based Path Pattern Cardinality Estimator (SPACE). Our approach treats path patterns as sequences of node labels and edge types and assign similar cardinalities to path patterns with similar node and edge order. SPACE uses a dual approach: it encodes the sequence of nodes and edges to capture structural characteristics of the path pattern, while also incorporating a cardinality-based encoding to integrate cardinality information throughout learning. In a comprehensive experimental evaluation, we show that our method outperforms the state of the art in terms of both accuracy (Q-error) and training time.