Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process

Deep Learning (DL) has achieved great success in many real applications. Despite its success, there are some main problems when deploying advanced DL models in database systems, such as hyper-parameters tuning, the risk of overfitting, and lack of prediction uncertainty. In this paper, we study a lightweight and accurate cardinality estimation for SQL queries, which is also uncertainty-aware. By lightweight, we mean that we can train a DL model in a few seconds. With uncertainty ensured, it becomes possible to update the estimator to improve its prediction in areas with high uncertainty. The approach we explore is different from the direction of deploying sophisticated DL models as cardinality estimators in database systems. We employ Bayesian deep learning (BDL), which serves as a bridge between Bayesian inference and deep learning. The prediction distribution by BDL provides principled uncertainty calibration for the prediction. In addition, when the network width of a BDL model goes to infinity, the model performs equivalent to Gaussian Process (GP). This special class of BDL, known as Neural Network Gaussian Process (NNGP), inherits the advantages of Bayesian approach while keeping universal approximation of neural networks, and can utilize a much larger model space to model distribution-free data as a nonparametric model. We show our NNGP estimator achieves high accuracy, is built fast, and is robust to query workload shift, in our extensive performance studies by comparing with existing learned estimators. We also confirm the effectiveness of NNGP by integrating it into PostgreSQL.