The Case for Automatic Database Administration using Deep Reinforcement Learning

01/17/2018
by   Ankur Sharma, et al.
0

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.

READ FULL TEXT
research
06/16/2020

Index Selection for NoSQL Database with Deep Reinforcement Learning

We propose a new approach of NoSQL database index selection. For differe...
research
07/19/2022

Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning

Distributed file systems are widely used nowadays, yet using their defau...
research
08/10/2021

A Survey on Deep Reinforcement Learning for Data Processing and Analytics

Data processing and analytics are fundamental and pervasive. Algorithms ...
research
02/17/2023

DMSConfig: Automated Configuration Tuning for Distributed IoT Message Systems Using Deep Reinforcement Learning

The Distributed Messaging Systems (DMSs) used in IoT systems require tim...
research
05/10/2022

On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer

Autonomously trained agents that are supposed to play video games reason...
research
09/20/2022

Collisionless Pattern Discovery in Robot Swarms Using Deep Reinforcement Learning

We present a deep reinforcement learning-based framework for automatical...
research
04/02/2019

Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Ware...

Please sign up or login with your details

Forgot password? Click here to reset