A Novel Framework Using Deep Reinforcement Learning for Join Order Selection
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909427117850624 |
|---|---|
| author | Liu, Chang Kamali, Amin Kantere, Verena Zuzarte, Calisto Corvinelli, Vincent |
| author_facet | Liu, Chang Kamali, Amin Kantere, Verena Zuzarte, Calisto Corvinelli, Vincent |
| contents | 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 GTDD, a novel framework that integrates Graph Neural Networks (GNN), Treestructured Long Short-Term Memory (Tree LSTM), and DuelingDQN. We conduct a series of experiments that demonstrate a clear advantage of GTDD over state-of the-art techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10253 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Novel Framework Using Deep Reinforcement Learning for Join Order Selection Liu, Chang Kamali, Amin Kantere, Verena Zuzarte, Calisto Corvinelli, Vincent Databases 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 GTDD, a novel framework that integrates Graph Neural Networks (GNN), Treestructured Long Short-Term Memory (Tree LSTM), and DuelingDQN. We conduct a series of experiments that demonstrate a clear advantage of GTDD over state-of the-art techniques. |
| title | A Novel Framework Using Deep Reinforcement Learning for Join Order Selection |
| topic | Databases |
| url | https://arxiv.org/abs/2412.10253 |