Learning Index Selection with Structured Action Spaces

Welborn, Jeremy; Schaarschmidt, Michael; Yoneki, Eiko

doi:10.48550/arxiv.1909.07440

Cited by 2 publications

(3 citation statements)

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“…Configuring the database management system through the application programming interface (API) is called external tuning, and embedding algorithms into the database management system is called internal tuning. Integrating ML technology into index selection is part of internal tuning and is one of the most critical parts of achieving database self-tuning [17,18]. For example, Ge et al [19] designed a distributed prediction-randomness framework for the evolutionary dynamic multiobjective partitioning optimization of databases.…”

Section: Related Workmentioning

confidence: 99%

RBOIRA: Integrating Rules and Reinforcement Learning to Improve Index Recommendation

Yu,

You,

Niu

et al. 2023

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The index is one of the most effective ways to improve the database query performance. The expert-based index recommendation approach cannot adjust the index configuration in real time. At the same time, reinforcement learning can automatically update the index and improve the recommended configuration by leveraging expert experience.OBJECTIVES: This paper proposes the RBOIRA, which combines rules and reinforcement learning to recommend the optimal index configuration for a set of workloads in a dynamic database.METHODS: Firstly, RBOIRA designed three heuristic rules for pruning index candidates. Secondly, it uses reinforcement learning to recommend the optimal index configuration for a set of workloads in the database. Finally, we conducted extensive experiments to evaluate RBOIRA using the TPC-H database benchmark.RESULTS: RBOIRA recommends index configurations with superior performance compared to the baselines we define and other reinforcement learning methods used in related work and also has robustness in different database sizes.

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Section: Related Workmentioning

confidence: 99%

RBOIRA: Integrating Rules and Reinforcement Learning to Improve Index Recommendation

Yu,

You,

Niu

et al. 2023

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

show abstract

“…Sadri et al [77] utilize DRL to select the index for a cluster database where both query processing and load balancing are considered. Welborn et al [108] optimize the action space design by introducing task-specific knowledge for index selection task in the database. However, these works only consider the situation where single-column indexes are built.…”

Section: Database Index Selectionmentioning

confidence: 99%

“…Additionally, the process of reward calculation can involve costly data collection and computation in the data systems optimization. Currently, many works rely on experimental exploration and experience to formulate MDP while some works exploit domain knowledge to improve the MDP formulation by injecting task-specific knowledge into action space [108]. Generally, MDP can influence computational complexity, data required, and algorithm performance.…”

Section: Mdp Formulation and Lack Of Justificationmentioning

confidence: 99%

A Survey on Deep Reinforcement Learning for Data Processing and Analytics

Cai¹,

Cui²,

Xiong³

et al. 2021

Preprint

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Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in data processing and analytics where many algorithm designs have incorporated heuristics and general rules from human knowledge and experience to improve their effectiveness. Recently, reinforcement learning, deep reinforcement learning (DRL) in particular, is increasingly explored and exploited in many areas because it can learn better strategies in complicated environments it is interacting with than statically designed algorithms. Motivated by this trend, we provide a comprehensive review of recent works focusing on utilizing deep reinforcement learning to improve data processing and analytics. First, we present an introduction to key concepts, theories, and methods in deep reinforcement learning. Next, we discuss deep reinforcement learning deployment on database systems, facilitating data processing and analytics in various aspects, including data organization, scheduling, tuning, and indexing. Then, we survey the application of deep reinforcement learning in data processing and analytics , ranging from data preparation, natural language interface to healthcare, fintech, etc. Finally, we discuss important open challenges and future research directions of using deep reinforcement learning in data processing and analytics.

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Learning Index Selection with Structured Action Spaces

Cited by 2 publications

References 0 publications

RBOIRA: Integrating Rules and Reinforcement Learning to Improve Index Recommendation

RBOIRA: Integrating Rules and Reinforcement Learning to Improve Index Recommendation

A Survey on Deep Reinforcement Learning for Data Processing and Analytics

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