GACE: Graph-Attention-Network-Based Cardinality Estimator.

Cardinality estimation plays a vital role in query optimizer, the key factors challenge its accuracy are join-crossing correlations between different attributes. Traditional estimation techniques provide poor estimation quality for complex queries with many joins for the lack of correlations. Recently, much work has shown that machine learning based methods overcome the challenge to a certain extent. However, the existing learning-based approaches have two major downsides. First, lack of explicit utilization of the effective and explainable feature: conjunctive predicates between different attributes. Second, the dynamic information associated with the dataset status is not used to encode the static query structure. In this paper, we propose GACE, a cardinality estimator based on Graph Attention Network (GAT) to capture join-crossing correlations between attributes from join graphs constructed on conjunctive predicates. GACE leverages the reliable features Base Table Selectivity and Join Selectivity obtained through traditional techniques to enrich the encoding of query structure and track the status alterations of dataset. Taking the regularity of join graphs into account, a GAT model with batch training supporting is implemented to sufficiently decrease the overhead of training. The results of our empirical evaluation on real-world dataset (IMDb) demonstrate that GACE achieves improvement in quality of cardinality estimation, especially for more joins.