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Query Centric Partitioning and Allocation for Partially Replicated Database Systems
Citation key RablJ2017
Author Tilmann Rabl, Arne Jacobsen
Pages 315-330.
Year 2017
DOI http://dx.doi.org/10.1145/3035918.3064052
Journal In Proceedings of the 2017 ACM International Conference on Management of Data (SIGMOD '17). ACM, New York, NY, USA,
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.
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