  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.
