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CloudMdsQL: querying heterogeneous cloud data stores with a common language

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Abstract

The blooming of different cloud data management infrastructures, specialized for different kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a common programming paradigm. In this paper, we present the design of a cloud multidatastore query language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational and NoSQL) within a single query that may contain embedded invocations to each data store’s native query interface. The query engine has a fully distributed architecture, which provides important opportunities for optimization. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping. Our experimental validation, with three data stores (graph, document and relational) and representative queries, shows that CloudMdsQL satisfies the five important requirements for a cloud multidatastore query language.

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Acknowledgments

Work partially funded by the European Commission through the CoherentPaaS FP7 Project funded under contract FP7-611068 [5]. We want to thank Norbert Martínez-Bazan for his contributions on the first version of the CloudMdsQL query engine. We also thank the editor and reviewers for their careful readings and useful suggestions that helped improving our design and the paper. The work of Prof. Ricardo Jimenez was also partially funded by the Regional Government of Madrid (CAM) under Project Cloud4BigData (S2013/ICE-2894) cofunded by ESF & ERDF, and the Spanish Research Council (MICCIN) under Project BigDataPaaS (TIN2013-46883).

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Authors and Affiliations

  1. Zenith team, Inria, Montpellier, France

    Boyan Kolev, Patrick Valduriez & Carlyna Bondiombouy

  2. Universidad Politecnica de Madrid (UPM) and LeanXcale, Madrid, Spain

    Ricardo Jiménez-Peris

  3. Sparsity Technologies, Barcelona, Spain

    Raquel Pau

  4. INESC, Braga, Portugal

    José Pereira

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  1. Boyan Kolev
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  2. Patrick Valduriez
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  3. Carlyna Bondiombouy
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  4. Ricardo Jiménez-Peris
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  5. Raquel Pau
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  6. José Pereira
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Correspondence to Boyan Kolev.

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Kolev, B., Valduriez, P., Bondiombouy, C. et al. CloudMdsQL: querying heterogeneous cloud data stores with a common language. Distrib Parallel Databases 34, 463–503 (2016). https://doi.org/10.1007/s10619-015-7185-y

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