Parallel Query Processing in Database on Multicore System

The growth in data volume poses a challenge to query processing. On the other hand, advancements in technology have made multicore CPUs available in many inexpensive servers. so parallel query based on multicore CPU is undoubtedly a solution for query acceleration and cost reduction. In this paper, a parallel query method is proposed and implemented, which rewrites the query execution plan and replaces the original serial scanning operator with a parallel driver operator under the premise that most of the operators do not change, so as to maximize the utilization of CPU and disk resources. Experiments show that it can achieve approximately a 2x speed increase in query processing.

Authors

Institutions

Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1109/eiecs63941.2024.10800366
Primary Topic
Advanced Computational Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Parallel Query Processing in Database on Multicore System

Zhen Liu, Wenjie Gao, Yifei Zhang, Guiyu Lai
Advanced Computational Techniques and Applications
article

Parallel Query Processing in Database on Multicore System

Zhen Liu, Wenjie Gao, Yifei Zhang, Guiyu Lai
article en

Abstract

The growth in data volume poses a challenge to query processing. On the other hand, advancements in technology have made multicore CPUs available in many inexpensive servers. so parallel query based on multicore CPU is undoubtedly a solution for query acceleration and cost reduction. In this paper, a parallel query method is proposed and implemented, which rewrites the query execution plan and replaces the original serial scanning operator with a parallel driver operator under the premise that most of the operators do not change, so as to maximize the utilization of CPU and disk resources. Experiments show that it can achieve approximately a 2x speed increase in query processing.

Chinese Academy of Engineering (CN)
Openalex Percentile: Top 78%
Advanced Computational Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.