Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …

Recent research has shown that learned models can outperform state-of-the-art index
structures in size and lookup performance. While this is a very promising result, existing …

Configuration knobs of database systems are essential to achieve high throughput and low
latency. Recently, automatic tuning systems using machine learning methods (ML) have …

We introduce Deep Sketches, which are compact models of databases that allow us to
estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep …

Database administrators (DBAs) were traditionally responsible for optimizing the on-premise
database workloads. However, with the rise of cloud data services, where cloud providers …

There has been a lot of excitement around using machine learning to improve the
performance and usability of database systems. However, few of these techniques have …

While estimating the result size of a group-by operation on a base table is hard on its own,
the presence of selections makes this problem increasingly difficult to solve. We show that …

In this extended abstract, we propose a new technique for query scheduling with the explicit
goal of reducing disk reads and thus implicitly increasing query performance. We introduce …

Formulating efficient SQL queries requires several cycles of tuning and execution. We
examine methods that can accelerate and improve this interaction by providing insights …

Performing a detailed workload analysis is a crucial step in determining the feasibility,
timeline and cost of a major data warehouse replatforming project, ie, migration from one …