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
2 LeanXcale [Madrid]
3 Sparcity Technologies [Barcelone]
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.
Keywords : Benchmark Cloud Heterogeneous data Polystore
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Conference papers
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Boyan Kolev, Raquel Pau, Oleksandra Levchenko, Patrick Valduriez, Ricardo Jiménez-Peris, et al.. Benchmarking Polystores: the CloudMdsQL Experience. Big Data, Dec 2016, Washington, DC, United States. pp.2574-2579, ⟨10.1109/BigData.2016.7840899⟩. ⟨lirmm-01415582⟩

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