Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII

Volume 9940 of the series Lecture Notes in Computer Science pp 48-74


Multistore Big Data Integration with CloudMdsQL

  • Carlyna BondiombouyAffiliated withInria and LIRMM, University of Montpellier
  • , Boyan KolevAffiliated withInria and LIRMM, University of Montpellier Email author 
  • , Oleksandra LevchenkoAffiliated withInria and LIRMM, University of Montpellier
  • , Patrick ValduriezAffiliated withInria and LIRMM, University of Montpellier

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Multistore systems have been recently proposed to provide integrated access to multiple, heterogeneous data stores through a single query engine. In particular, much attention is being paid on the integration of unstructured big data typically stored in HDFS with relational data. One main solution is to use a relational query engine that allows SQL-like queries to retrieve data from HDFS, which requires the system to provide a relational view of the unstructured data and hence is not always feasible. In this paper, we propose a functional SQL-like query language (based on CloudMdsQL) that can integrate data retrieved from different data stores, to take full advantage of the functionality of the underlying data processing frameworks by allowing the ad-hoc usage of user defined map/filter/reduce operators in combination with traditional SQL statements. Furthermore, our solution allows for optimization by enabling subquery rewriting so that bind join can be used and filter conditions can be pushed down and applied by the data processing framework as early as possible. We validate our approach through implementation and experimental validation with three data stores and representative queries. The experimental results demonstrate the usability of the query language and the benefits from query optimization.