Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Abstract and 1. Introduction Related Works
2.1 Traditional Index Selection Approaches
2.2 RL-based Index Selection Approaches


Index Selection Problem


Methodology
4.1 Formulation of the DRL Problem
4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection


System Framework of IA2
5.1 Preprocessing Phase
5.2 RL Training and Application Phase


Experiments
6.1 Experimental Setting
6.2 Experimental Results
6.3 End-to-End Performance Comparison
6.4 Key Insights


Conclusion and Future Work, and References Related Works
2.1 Traditional Index Selection Approaches
2.2 RL-based Index Selection Approaches Related Works Related Works 2.1 Traditional Index Selection Approaches 2.1 Traditional Index Selection Approaches 2.2 RL-based Index Selection Approaches 2.2 RL-based Index Selection Approaches Index Selection Problem Index Selection Problem Index Selection Problem Index Selection Problem Methodology
4.1 Formulation of the DRL Problem
4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection Methodology Methodology 4.1 Formulation of the DRL Problem 4.1 Formulation of the DRL Problem 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection System Framework of IA2
5.1 Preprocessing Phase
5.2 RL Training and Application Phase System Framework of IA2 System Framework of IA2 System Framework of IA2 5.1 Preprocessing Phase 5.1 Preprocessing Phase 5.2 RL Training and Application Phase 5.2 RL Training and Application Phase Experiments
6.1 Experimental Setting
6.2 Experimental Results
6.3 End-to-End Performance Comparison
6.4 Key Insights Experiments Experiments 6.1 Experimental Setting 6.1 Experimental Setting 6.2 Experimental Results 6.2 Experimental Results 6.3 End-to-End Performance Comparison 6.3 End-to-End Performance Comparison 6.4 Key Insights 6.4 Key Insights Conclusion and Future Work, and References Conclusion and Future Work, and References Conclusion and Future Work, and References Conclusion and Future Work, and References 5.1 Preprocessing Phase The preprocessing phase is critical for establishing a solid foundation for IA2’s operation. It consists of two components: Workload Model: Enhanced by the underlying optimizer and what-if cost models, the workload model captures database workload variabilities. It integrates four essential components: Query Plan features, reflecting database reactions; current index configurations; Meta information about database configurations and budget; and embedded tokenized queries. This model is crucial for providing accurate state representations to the downstream DRL training task, significantly boosting IA2’s generalization capabilities across diverse workloads. Workload Model: Index Candidates Enumerator: This component extends beyond exhaustive enumeration, employing validation rules and restrictions to discern the relevance among queries. By leveraging permutations and heuristic rules that cater to generic operators and workload structures, the Enumerator crafts index candidates. This approach, inspired by and integrating advancements from Lan et al. [7] , enriches the selection pool with a broader array of indexing strategies, poised to optimize performance across varying scenarios. The generated index candidates form the raw action space for the downstream RL task, laying a foundational step for IA2’s decision-making process in selecting optimal indexes Index Candidates Enumerator: Authors:
(1) Taiyi Wang, University of Cambridge, Cambridge, United Kingdom (Taiyi.Wang@cl.cam.ac.uk);
(2) Eiko Yoneki, University of Cambridge, Cambridge, United Kingdom (eiko.yoneki@cl.cam.ac.uk). Authors: Authors: (1) Taiyi Wang, University of Cambridge, Cambridge, United Kingdom (Taiyi.Wang@cl.cam.ac.uk); (2) Eiko Yoneki, University of Cambridge, Cambridge, United Kingdom (eiko.yoneki@cl.cam.ac.uk). This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

IA2 Preprocessing: Establishing the Foundation for Index Selection

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

3D Mapping Initialization: Using RGB-D Images and Camera Parameters

It Is Okay If You Don't Know What You Like. We Do (feat. Deep Recommendation Algorithms)

10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020

10 Computer Vision Startups on Product Hunt with the Most Upvotes

10 Best Entry Level Machine Learning Tutorials

10 Best + Free Machine Learning Courses Collection

3D Mapping Initialization: Using RGB-D Images and Camera Parameters

It Is Okay If You Don't Know What You Like. We Do (feat. Deep Recommendation Algorithms)

10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020

10 Computer Vision Startups on Product Hunt with the Most Upvotes

10 Best Entry Level Machine Learning Tutorials

10 Best + Free Machine Learning Courses Collection

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps