Map-Reduce Implementations: Survey and Performance Comparison

Abstract

Map Reduce has gained remarkable significance as a prominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytics where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using MapReduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the MapReduce framework. In this survey, different MapReduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on MapReduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.