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Ttito, J., (2022). Query co-planning for shared execution in key-value stores [Tesis, Universidad Católica San Pablo]. https://hdl.handle.net/20.500.12590/17104
Ttito, J., Query co-planning for shared execution in key-value stores [Tesis]. PE: Universidad Católica San Pablo; 2022. https://hdl.handle.net/20.500.12590/17104
@misc{renati/782094,
title = "Query co-planning for shared execution in key-value stores",
author = "Ttito Amezquita, Josue Joel",
publisher = "Universidad Católica San Pablo",
year = "2022"
}
Título: Query co-planning for shared execution in key-value stores
Autor(es): Ttito Amezquita, Josue Joel
Asesor(es): Marroquin Mogrovejo, Renato Javier
Palabras clave: Key-value stores; Range queries; Bases de Datos; Optimización de cargas de trabajo compartido
Campo OCDE: http://purl.org/pe-repo/ocde/ford#1.02.01
Fecha de publicación: 2022
Institución: Universidad Católica San Pablo
Resumen: Large amounts of data are being stored and queried using different data
models. For each of these models, there are specialized data stores which
are then accessed concurrently by many different applications. For instance,
key-value stores provide a simple data model of key and value pairs. Thus,
the simplicity of their read and write interface. Additionally, they provide
other operations such as full and range scans. However, along with its
simplicity, key-value stores impose some limitations when trying to optimize
data access. In this work, we study how to minimize the data movement
when executing a large number of range queries on key-value stores. This
is based on the observation that when accessing a common dataset, there
is usually a (possibly large) overlap among queries accessing it. Thus, to
accomplish this, we use shared-workload optimization techniques to execute
a group of queries together. We analyze different data structures suitable
for co-planning multiple range queries together in order to reduce the total
amount of data transferred. Our results show that by co-planning a group
of range queries we reduce the total execution time of a query workload
Enlace al repositorio: https://hdl.handle.net/20.500.12590/17104
Disciplina académico-profesional: Ciencia de la Computación
Institución que otorga el grado o título: Universidad Católica San Pablo. Departamento de Ciencia de la Computación
Grado o título: Maestro en Ciencia de la Computación
Jurado: José Eduardo Ochoa Luna; José Eduardo Talavera; Sergio Lifschitz; Lewis John McGibbney
Fecha de registro: 25-mar-2022
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