Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/38257

TitleWorkload-aware table splitting for NoSQL
Author(s)Cruz, Francisco
Oliveira, Rui Carlos Mendes de
Maia, Francisco
Vilaça, Ricardo
KeywordsDistributed systems
NoSQL
Table splitting
Issue date2014
PublisherACM
Abstract(s)Massive scale data stores, which exhibit highly desirable scalability and availability properties are becoming pivotal systems in nowadays infrastructures. Scalability achieved by these data stores is anchored on data independence; there is no clear relationship between data, and atomic inter-node operations are not a concern. Such assumption over data allows aggressive data partitioning. In particular, data tables are horizontally partitioned and spread across nodes for load balancing. However, in current versions of these data stores, partitioning is either a manual process or automated but simply based on table size. We argue that size based partitioning does not lead to acceptable load balancing as it ignores data access patterns, namely data hotspots. Moreover, manual data partitioning is cumbersome and typically infeasible in large scale scenarios. In this paper we propose an automated table splitting mechanism that takes into account the system workload. We evaluate such mechanism showing that it simple, non-intrusive and effective.
TypeConference paper
URIhttp://hdl.handle.net/1822/38257
ISBN978-1-4503-2469-4
DOI10.1145/2554850.2555027
Publisher versionhttp://dl.acm.org/citation.cfm?id=2555027
Peer-Reviewedyes
AccessOpen access
Appears in Collections:HASLab - Artigos em atas de conferências internacionais (texto completo)

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