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espanolEn la busqueda de soluciones MapReduce modulares, la principal meta de este trabajo es aplicar Hadoop y AspectJ para la definicion de funciones Aspect-Combine. MapReduce es un enfoque de computacion para trabajar con grandes volumenes de datos (BigData) en un entorno distribuido, con altos niveles de abstraccion y con el uso ordenado de funciones Map y Reduce, la primera de ellas para el mapeo o identificacion de datos relevantes y la segunda para resumir datos y resultados finales. Hadoop es una aplicacion libre de MapReduce que permite la definicion de funciones Combine para la agrupacion local de datos en la fase de Mapeo y asi minimizar el trafico de informacion entre Mapper y Reducer. Sin embargo, la ejecucion de Combine no es garantizada en Hadoop, asunto que motivo este trabajo. Como resultado, se alcanza un mayor grado de modularizacion desde un punto de vista teorico, y desde el punto de vista practico tambien existen mejoras en rendimiento. EnglishJustly, in the search for modular MapReduce solutions, the main goal of this work is to apply Hadoop and AspectJ for the definition of Aspect-Combine functions. MapReduce is a computing approach to work with large volumes of data (BigData) in a distributed environment, with high levels of abstraction and the ordered use of Map and Reduce functions, the first one for mapping or identifying relevant data and the second for resuming data and final results. In a MapReduce system, Mapper and Reducer nodes implement the Map and Reduce functions respectively. Hadoop is a free application of MapReduce that allows the definition of Combine functions. However, the execution of Combine is not guaranteed in Hadoop. This problem motivated this work. As a result, a greater degree of modularization is reached from a theoretical point of view. From the practical point of view there are also improvements in performance.

En la Búsqueda de Soluciones MapReduce Modulares para el Trabajo con BigData: Hadoop Orientado a Aspectos