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Full Description
This monograph presents recent progress on using machine learning techniques to improve query optimizers in database systems. Centering around a generic paradigm of learned query optimizers, the publication covers several lines of efforts on rebuilding or aiding important components in query optimizers (i.e., cardinality estimators, cost models, and plan enumerators) with machine learning.
Some important machine learning tools that have recently been developed are introduced, which are useful for query optimization, and it is shown how they are adapted for sub-tasks of query optimization.
This monograph is for readers who are already familiar with query optimization and who are eager to understand what machine learning techniques can be helpful, and how to apply them with examples and necessary details. The text is also relevant for machine learning researchers who want to expand their research agendas to helping database systems with machine learning techniques. Some open research challenges are also discussed with the goal of making learned query optimizers truly applicable in production.
Contents
1. Introduction
2. Learned Cost Models
3. Exploring Plan Space
4. Open Research Challenges
References
