Query optimization remains one of the most important and well-studied problems in database systems. However, traditional query optimizers are complex heuristically-driven systems, requiring large amounts of time to tune for a particular database and requiring even more time to develop and maintain in the first place. In this vision paper, we argue that a new type of query optimizer, based on deep reinforcement learning, can drastically improve on the state-of-the-art. We identify potential complications for future research that integrates deep learning with query optimization, and we describe three novel deep learning based approaches that can lead the way to end-to-end learning-based query optimizers.
@article{arxiv.1809.10212,
title = {Towards a Hands-Free Query Optimizer through Deep Learning},
author = {Ryan Marcus and Olga Papaemmanouil},
journal= {arXiv preprint arXiv:1809.10212},
year = {2018}
}