Benchmarking Polystores: the CloudMdsQL Experience

dc.contributor.author Kolev,B en
dc.contributor.author Pau,R en
dc.contributor.author Levchenko,O en
dc.contributor.author Valduriez,P en
dc.contributor.author Jimenez Peri,R en
dc.contributor.author José Orlando Pereira en
dc.date.accessioned 2018-01-17T15:38:52Z
dc.date.available 2018-01-17T15:38:52Z
dc.date.issued 2016 en
dc.description.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. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/6746
dc.identifier.uri http://dx.doi.org/10.1109/BigData.2016.7840899 en
dc.language eng en
dc.relation 5602 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Benchmarking Polystores: the CloudMdsQL Experience en
dc.type conferenceObject en
dc.type Publication en
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