Haibo Xiu

Conference Lecture Notes in Computer Science · January 1, 2026 Modern database systems are expected to handle dynamic data whose characteristics may evolve over time. Many popular database benchmarks are limited in their ability to evaluate this dynamic aspect of the database systems. Those that use synthetic data gen ... Full text Cite

PAR2QO: Parametric Penalty-Aware Robust Query Optimization

Conference Proceedings of the VLDB Endowment · January 1, 2025 Parametric Query Optimization (PQO) is an important problem in database systems, yet existing approaches suffer from high training costs, sensitivity to estimation errors, and vulnerability to severe performance regressions. This paper introduces PAR2QO (P ... Full text Cite

veDB-HTAP: a Highly Integrated, Efficient and Adaptive HTAP System

Conference Proceedings of the VLDB Endowment · January 1, 2025 In this paper, we describe veDB-HTAP, a highly integrated, efficient, and adaptive HTAP system recently built in ByteDance. veDB-HTAP adopts a highly integrated system architecture by leveraging the Secondary Engine mechanism provided by MySQL and provides ... Full text Cite

Hint-QPT: Hints for Robust Query Performance Tuning

Conference Proceedings of the VLDB Endowment · January 1, 2025 Query optimizers rely heavily on selectivity estimates to choose efficient execution plans, but inaccuracies in these estimates often result in poor query performance. We introduce Hint-QPT (Hints for Robust Query Performance Tuning), an interactive tool d ... Full text Cite

PARQO: Penalty-Aware Robust Plan Selection in Query Optimization

Conference Proceedings of the VLDB Endowment · January 1, 2024 The effectiveness of a query optimizer relies on the accuracy of selectivity estimates. The execution plan generated by the optimizer can be extremely poor in reality due to uncertainty in these estimates. This paper presents PARQO (Penalty-Aware Robust Pl ... Full text Cite

Selectivity Functions of Range Queries are Learnable

Conference Proceedings of the ACM SIGMOD International Conference on Management of Data · June 11, 2022 This paper explores the use of machine learning for estimating the selectivity of range queries in database systems. Using classic learning theory for real-valued functions based on shattering dimension, we show that the selectivity function of a range spa ... Full text Cite