Query optimizers in RDBMSs search for execution plans expected to be optimal for given queries. They use parameter estimates, often inaccurate, and make assumptions that may not hold in practice. Consequently, they may select plans that are suboptimal at runtime, if estimates and assumptions are not valid. Therefore, they do not sufficiently support robust query optimization. Using ML to improve data systems has shown promising results for query optimization. Inspired by this, we propose Robust Query Optimizer, (Roq), a holistic framework based on a risk-aware learning approach. Roq includes a novel formalization of the notion of robustness in the context of query optimization and a principled approach for its quantification and measurement based on approximate probabilistic ML. It also includes novel strategies and algorithms for query plan evaluation and selection. Roq includes a novel learned cost model that is designed to predict the cost of query execution and the associated risks and performs query optimization accordingly. We demonstrate that Roq provides significant improvements in robust query optimization compared with the state-of-the-art.

Join order selection is a sub-field of query optimization that aims to find the optimal join order for an SQL query with the minimum cost. The challenge lies in the exponentially growing search space as the number of tables increases, making exhaustive enumeration impractical. Traditional optimizers use static heuristics to prune the search space, but they often fail to adapt to changes or improve based on feedback from the DBMS. Recent research addresses these limitations with Deep Reinforcement Learning (DRL), allowing models to use feedback to dynamically search for better join orders and enhance performance over time. Existing research primarily focuses on capturing join order sequences and their representations at various levels, with limited comparative analysis of reinforcement learning methods. In this paper, we propose a novel framework, which integrates Graph Neural Networks (GNN), Tree-structured Long Short-Term Memory (Tree-LSTM), and dueling-DQN. We conduct a series of experiments to demonstrate the potential for improvement in DRL methods.

Query optimizers are an essential component of database management systems (DBMSs) as they search for an execution plan that is expected to be optimal for a given query. However, they commonly use parameter estimates that are often inaccurate and make assumptions that may not hold in practice. Consequently, the optimizer may select sub-optimal execution plans at runtime, when these estimates and assumptions are not valid, which may result in poor query performance. Therefore, query optimizers do not adequately support the robustness of the database system. In this tutorial, we explore the notion of robustness in the context of query optimization, as well as how it is evaluated or even further supported. Firstly, we provide a comprehensive definition for the notion of robustness in this context that accounts for risks associated with execution plans and inaccurate parameter estimates as well as the limitations of the cost models. Next, we review the approaches proposed in the literature to address the issue of robustness, including techniques that rely on query re-optimization, discovering parameters, quantifying robustness, as well as recent techniques that employ machine learning. We focus on comparing traditional cost-model-based methods with modern ML-based techniques in terms of their ability to tackle the challenge of robustness in query optimization. Finally, we discuss the limitations and gaps in the current literature and provide some recommendations for future research directions.