Search for: All records

Query optimizers must efficiently choose a query plan using noisy estimates of cardinalities. We study robust query optimization, where the optimizer is made aware of the uncertainty in cardinality estimates and needs to select the most ''robust'' plan. A key empirical observation is that a small set of plans often suffices as robust plan candidates for all queries following a given template. We formalize this phenomenon by introducing coresets of plans and extend this notion to robust coresets, which incorporate uncertainty. We prove positive and negative results on the sizes of such coresets for common cost-function classes. Because our lower bounds suggest that the size of a coreset can be large in the worst case, we exploit the fact that in practice queries arise from workload distributions, rather than being arbitrary. We present algorithms that construct workload-aware coresets whose size and performance closely match those of the optimal workload-specific coresets. 

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 generators often fail to capture the complexity and unpredictable nature of real data, while most real-world datasets are static and difficult to create high-volume, realistic updates for. This paper introduces CrypQ, a database benchmark leveraging dynamic, public Ethereum blockchain data. CrypQ offers a high-volume, ever-evolving dataset reflecting the unpredictable nature of a real and active cryptocurrency market. We detail CrypQ’s schema, procedures for creating data snapshots and update sequences, and a suite of relevant SQL queries. As an example, we demonstrate CrypQ’s utility in evaluating cost-based query optimizers on complex, evolving data distributions with real-world skewness and dependencies. 

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 (PARametric Penalty-Aware Robust Query Optimization), a system that integrates robust query optimization into PQO. PAR²QO strategically obtains plans from a well-balanced set of probe locations informed by the workload, and caches them as plan-penalty profiles. At runtime, PAR²QO selects the plan with the lowest expected penalty, explicitly accounting for selectivity uncertainties. Extensive experiments show that PAR²QO delivers significant speedups over existing methods while ensuring robustness against performance degradation. Additionally, we introduceCARVER, a workload generator aimed at covering possible cardinalities of subqueries. Not only does CARVER provide a more comprehensive way to evaluate PQO methods, but when used for training learned methods, it can also enhance their generalizability and stability. 

Query optimizers rely heavily on selectivity estimates to choose efficient execution plans, but inaccuracies in these estimates often result in poor query performance. We introduceHint-QPT(Hints for RobustQueryPerformanceTuning), an interactive tool designed to help users diagnose and improve query performance. Hint-QPT proactively recommends robust plans that are resilient to uncertainty in selectivity estimates, identifies sensitive subqueries for which selectivity estimation errors greatly affect plan quality, and provides intuitive interfaces for targeted selectivity adjustments. Users can either choose the recommended robust plans for execution, or acquire additional statistics on the identified sensitive subqueries to tune query performance. Moreover, Hint-QPT visualizes the alternative execution plans and their costs under uncertainty, helping users to better understand their robustness.