Benchmarking Polystores: the CloudMdsQL Experience

Boyan Kolev 1, 2 Raquel Pau 3 Oleksandra Levchenko 1 Patrick Valduriez 1 Ricardo Jiménez-Peris 2 José Pereira 2
1 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The CloudMdsQL polystore provides integrated access to multiple heterogeneous data stores, such as RDBMS, NoSQL or even HDFS through a big data analytics framework such as MapReduce or Spark. The CloudMdsQL language is a functional SQL-like query language with a flexible nested data model. A major capability is to exploit the full power of each of the underlying data stores by allowing native queries to be expressed as functions and involved in SQL statements. The CloudMdsQL polystore has been validated with a good number of different data stores: HDFS, key-value, document, graph, RDBMS and OLAP engine. In this paper, we introduce the benchmarking of the CloudMdsQL polystore and evaluate the performance benefits of important features enabled by the query language and engine.
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Communication dans un congrès
Vijay Gadepally. International Conference on Big Data, Dec 2016, Washington, DC, United States. IEEE Computing Society, IEEE BigData 2016: Workshop on Methods to Manage Heterogeneous Big Data and Polystore Databases, 2017, 〈https://sites.google.com/site/polystoreworkshop/home〉. 〈10.1109/BigData.2016.7840899〉
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Dernière modification le : jeudi 24 mai 2018 - 15:59:21
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Boyan Kolev, Raquel Pau, Oleksandra Levchenko, Patrick Valduriez, Ricardo Jiménez-Peris, et al.. Benchmarking Polystores: the CloudMdsQL Experience. Vijay Gadepally. International Conference on Big Data, Dec 2016, Washington, DC, United States. IEEE Computing Society, IEEE BigData 2016: Workshop on Methods to Manage Heterogeneous Big Data and Polystore Databases, 2017, 〈https://sites.google.com/site/polystoreworkshop/home〉. 〈10.1109/BigData.2016.7840899〉. 〈lirmm-01415582〉

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