Query centric partitioning and allocation for partially replicated database systems

Tilmann Rabl, Hans Arno Jacobsen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

A key feature of database systems is to provide transparent access to stored data. In distributed database systems, this includes data allocation and fragmentation. Transparent access introduces data dependencies and increases system complexity and inter-process communication. Therefore, many developers are exchanging transparency for better scalability using sharding and similar techniques. However, explicitly managing data distribution and data flow requires a deep understanding of the distributed system and the data access, and it reduces the possibilities for optimizations. To address this problem, we present an approach for efficient data allocation that features good scalability while keeping the data distribution transparent. We propose a workload-aware, query-centric, heterogeneity-aware analytical model. We formalize our approach and present an efficient allocation algorithm. The algorithm optimizes the partitioning and data layout for local query execution and balances the workload on homogeneous and heterogeneous systems according to the query history. In the evaluation, we demonstrate that our approach scales well in performance for OLTP- and OLAPstyle workloads and reduces storage requirements significantly over replicated systems while guaranteeing configurable availability.

Original languageEnglish
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages315-330
Number of pages16
ISBN (Electronic)9781450341974
DOIs
StatePublished - 9 May 2017
Externally publishedYes
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States
Duration: 14 May 201719 May 2017

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
VolumePart F127746
ISSN (Print)0730-8078

Conference

Conference2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
Country/TerritoryUnited States
CityChicago
Period14/05/1719/05/17

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