dc.creatorHerrera-Ramírez,José A.
dc.creatorTreviño-Villalobos,Marlen
dc.creatorVíquez-Acuña,Leonardo
dc.date2021-03-01
dc.date.accessioned2023-09-25T14:29:25Z
dc.date.available2023-09-25T14:29:25Z
dc.identifierhttp://www.scielo.sa.cr/scielo.php?script=sci_arttext&pid=S0379-39822021000100040
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8820898
dc.descriptionAbstract The design and implementation of services to handle geospatial data involves thinking about storage engine performance and optimization for the desired use. NoSQL and relational databases bring their own advantages; therefore, it is necessary to choose one of these options according to the requirements of the solution. These requirements can change, or some operations may be performed in a more efficient way on another database engine, so using just one engine means being tied to its features and work model. This paper presents a hybrid approach (NoSQL-SQL) to store geospatial data on MongoDB, which are replicated and mapped on a PostgreSQL database, using an open source tool called ToroDB Stampede; solutions then can take advantage from either NoSQL or SQL features, to satisfy most of the requirements associated to the storage engine performance. A descriptive analysis to explain the workflow of the replication and synchronization in both engines precedes the quantitative analysis by which it was possible to determine that a normal database in PostgreSQL has a shorter response time than to perform the query in PostgreSQL with the hybrid database. In addition, the type of geometry increases the update response time of a materialized view.
dc.formattext/html
dc.languageen
dc.publisherInstituto Tecnológico de Costa Rica
dc.relation10.18845/tm.v34i1.4822
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceRevista Tecnología en Marcha v.34 n.1 2021
dc.subjectDatabase
dc.subjectSQL
dc.subjectNoSQL
dc.subjectToroDB
dc.subjectMongoDB
dc.subjectPostgreSQL
dc.subjectreplication
dc.subjectmirroring
dc.titleHybrid storage engine for geospatial data using NoSQL and SQL paradigms
dc.typeinfo:eu-repo/semantics/article


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