Sampling-Based Query Re-Optimization

Despite of decades of work, query optimizers still make mistakes on "difficult" queries because of bad cardinality estimates, often due to the interaction of multiple predicates and correlations in the data. In this paper, we propose a low-cost post-processing step that can take a plan produced by the optimizer, detect when it is likely to have made such a mistake, and take steps to fix it. Specifically, our solution is a sampling-based iterative procedure that requires almost no changes to the original query optimizer or query evaluation mechanism of the system. We show that this indeed imposes low overhead and catches cases where three widely used optimizers (PostgreSQL and two commercial systems) make large errors.Comment: This is the extended version of a paper with the same title and authors that appears in the Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2016

10381 Summary and Abstracts Collection -- Robust Query Processing

Dagstuhl seminar 10381 on robust query processing (held 19.09.10 - 24.09.10) brought together a diverse set of researchers and practitioners with a broad range of expertise for the purpose of fostering discussion and collaboration regarding causes, opportunities, and solutions for achieving robust query processing. The seminar strove to build a unified view across the loosely-coupled system components responsible for the various stages of database query processing. Participants were chosen for their experience with database query processing and, where possible, their prior work in academic research or in product development towards robustness in database query processing. In order to pave the way to motivate, measure, and protect future advances in robust query processing, seminar 10381 focused on developing tests for measuring the robustness of query processing. In these proceedings, we first review the seminar topics, goals, and results, then present abstracts or notes of some of the seminar break-out sessions. We also include, as an appendix, the robust query processing reading list that was collected and distributed to participants before the seminar began, as well as summaries of a few of those papers that were contributed by some participants

CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads

Index tuning, i.e., selecting the indexes appropriate for a workload, is a crucial problem in database system tuning. In this paper, we solve index tuning for large problem instances that are common in practice, e.g., thousands of queries in the workload, thousands of candidate indexes and several hard and soft constraints. Our work is the first to reveal that the index tuning problem has a well structured space of solutions, and this space can be explored efficiently with well known techniques from linear optimization. Experimental results demonstrate that our approach outperforms state-of-the-art commercial and research techniques by a significant margin (up to an order of magnitude).Comment: VLDB201

Kepler: Robust Learning for Faster Parametric Query Optimization

Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance. We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer. Central to our method is Row Count Evolution (RCE), a novel plan generation algorithm based on perturbations in the sub-plan cardinality space. While previous approaches require accurate cost models, we bypass this requirement by evaluating candidate plans via actual execution data and training an ML model to predict the fastest plan given parameter binding values. Our models leverage recent advances in neural network uncertainty in order to robustly predict faster plans while avoiding regressions in query performance. Experimentally, we show that Kepler achieves significant improvements in query runtime on multiple datasets on PostgreSQL.Comment: SIGMOD 202

Robust Query Optimization Methods With Respect to Estimation Errors: A Survey

International audienceThe quality of a query execution plan chosen by a Cost-Based Optimizer (CBO) depends greatly on the estimation accuracy of input parameter values. Many research results have been produced on improving the estimation accuracy, but they do not work for every situation. Therefore, "robust query optimization" was introduced, in an effort to minimize the sub-optimality risk by accepting the fact that estimates could be inaccurate. In this survey, we aim to provide an overview of robust query optimization methods by classifying them into different categories, explaining the essential ideas, listing their advantages and limitations, and comparing them with multiple criteria

ReoptSMART: A Learning Query Plan Cache

The task of query optimization in modern relational database systems is important but can be computationally expensive. Parametric query optimization(PQO) has as its goal the prediction of optimal query execution plans based on historical results, without consulting the query optimizer. We develop machine learning techniques that can accurately model the output of a query optimizer. Our algorithms handle non-linear boundaries in plan space and achieve high prediction accuracy even when a limited amount of data is available for training. We use both predicted and actual query execution times for learning, and are the first to demonstrate a total net win of a PQO method over a state-of-the-art query optimizer for some workloads. ReoptSMART realizes savings not only in optimization time, but also in query execution time, for an over-all improvement by more than an order of magnitude in some cases

Adapting plan-based re-optimization of multiway join queries for streaming data

Master'sMASTER OF SCIENC

Optimization of Regular Path Queries in Graph Databases

Regular path queries offer a powerful navigational mechanism in graph databases. Recently, there has been renewed interest in such queries in the context of the Semantic Web. The extension of SPARQL in version 1.1 with property paths offers a type of regular path query for RDF graph databases. While eminently useful, such queries are difficult to optimize and evaluate efficiently, however. We design and implement a cost-based optimizer we call Waveguide for SPARQL queries with property paths. Waveguide builds a query planwhich we call a waveplan (WP)which guides the query evaluation. There are numerous choices in the con- struction of a plan, and a number of optimization methods, so the space of plans for a query can be quite large. Execution costs of plans for the same query can vary by orders of magnitude with the best plan often offering excellent performance. A WPs costs can be estimated, which opens the way to cost-based optimization. We demonstrate that Waveguide properly subsumes existing techniques and that the new plans it adds are relevant. We analyze the effective plan space which is enabled by Waveguide and design an efficient enumerator for it. We implement a pro- totype of a Waveguide cost-based optimizer on top of an open-source relational RDF store. Finally, we perform a comprehensive performance study of the state of the art for evaluation of SPARQL property paths and demonstrate the significant performance gains that Waveguide offers