Automatic configuration of the Cassandra database using irace

Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to...

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Vydané v:PeerJ. Computer science Ročník 7; s. e634
Hlavní autori: Silva-Muñoz, Moisés, Franzin, Alberto, Bersini, Hugues
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States PeerJ. Ltd 05.08.2021
PeerJ, Inc
PeerJ Inc
Predmet:
Algorithms
Artificial Intelligence
Automatic configuration
Cassandra
Configurations
Configurations (Computers)
Data Mining and Machine Learning
Data processing
Database administration
Design of experiments
Hyperparameter tuning
Machine learning
Methods
Optimization
Parameter tuning
Parameters
Performance evaluation
Software
Statistical methods
Statistical models
Workloads
United States
Databases
Automatic configuration
Parameter tuning
Cassandra
Hyperparameter tuning
ISSN:2376-5992, 2376-5992
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Shrnutí:Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.634