The Case for Automatic Database Administration using Deep Reinforcement Learning

TL;DR

This paper explores using deep reinforcement learning to automate database management tasks, specifically index selection, aiming to reduce human intervention and improve system tuning efficiency.

Contribution

It introduces a novel approach applying deep reinforcement learning to automate DBMS configuration and demonstrates its effectiveness in index recommendation tasks.

Findings

Deep reinforcement learning can effectively recommend indexes for workloads.

The approach reduces reliance on human intuition for database tuning.

Initial results show promising improvements in index selection accuracy.

Abstract

Like any large software system, a full-fledged DBMS offers an overwhelming amount of configuration knobs. These range from static initialisation parameters like buffer sizes, degree of concurrency, or level of replication to complex runtime decisions like creating a secondary index on a particular column or reorganising the physical layout of the store. To simplify the configuration, industry grade DBMSs are usually shipped with various advisory tools, that provide recommendations for given workloads and machines. However, reality shows that the actual configuration, tuning, and maintenance is usually still done by a human administrator, relying on intuition and experience. Recent work on deep reinforcement learning has shown very promising results in solving problems, that require such a sense of intuition. For instance, it has been applied very successfully in learning how to play complicated games with enormous search spaces. Motivated by these achievements, in this work we explore how deep reinforcement learning can be used to administer a DBMS. First, we will describe how deep reinforcement learning can be used to automatically tune an arbitrary software system like a DBMS by defining a problem environment. Second, we showcase our concept of NoDBA at the concrete example of index selection and evaluate how well it recommends indexes for given workloads.