Index Selection for NoSQL Database with Deep Reinforcement Learning

06/16/2020
by   Shun Yao, et al.
0

We propose a new approach of NoSQL database index selection. For different workloads, we select different indexes and their different parameters to optimize the database performance. The approach builds a deep reinforcement learning model to select an optimal index for a given fixed workload and adapts to a changing workload. Experimental results show that, Deep Reinforcement Learning Index Selection Approach (DRLISA) has improved performance to varying degrees according to traditional single index structures.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/15/2019

Progressive Neural Index Search for Database System

In database systems, index is the key component to support efficient sea...
01/17/2018

The Case for Automatic Database Administration using Deep Reinforcement Learning

Like any large software system, a full-fledged DBMS offers an overwhelmi...
08/15/2020

Automatic Storage Structure Selection for hybrid Workload

In the use of database systems, the design of the storage engine and dat...
07/25/2020

Automated Database Indexing using Model-free Reinforcement Learning

Configuring databases for efficient querying is a complex task, often ca...
04/05/2021

UDO: Universal Database Optimization using Reinforcement Learning

UDO is a versatile tool for offline tuning of database systems for speci...
04/02/2019

Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Ware...
03/04/2019

Opportunistic View Materialization with Deep Reinforcement Learning

Carefully selected materialized views can greatly improve the performanc...