DMCA
Dynamic Workload-Based Partitioning for Large-Scale Databases
| Citations: | 1 - 0 self |
BibTeX
@MISC{Liroz-gistau_dynamicworkload-based,
author = {Miguel Liroz-gistau and Reza Akbarinia and Esther Pacitti and Fabio Porto and Patrick Valduriez},
title = {Dynamic Workload-Based Partitioning for Large-Scale Databases},
year = {}
}
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Abstract
Abstract. Applications with very large databases, where data items are continuously appended, are becoming more and more common. Thus, the development of efficient workload-based data partitioning is one of the main requirements to offer good performance to most of those appli-cations that have complex access patterns, e.g. scientific applications. However, the existing workload-based approaches, which are executed in a static way, cannot be applied to very large databases. In this paper, we propose DynPart, a dynamic partitioning algorithm for continuously growing databases. DynPart efficiently adapts the data partitioning to the arrival of new data elements by taking into account the affinity of new data with queries and fragments. In contrast to existing static ap-proaches, our approach offers a constant execution time, no matter the size of the database, while obtaining very good partitioning efficiency. We validated our solution through experimentation over real-world data; the results show its effectiveness. 1
Keyphrases
dynamic workload-based partitioning large-scale database large database good performance new data element good partitioning efficiency complex access pattern main requirement data partitioning static way real-world data efficient workload-based data partitioning scientific application data item dynamic partitioning algorithm new data constant execution time static ap-proaches workload-based approach
